definition of speech recognition

Speech recognition, also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text, is a capability that enables a program to process human speech into a written format.

While speech recognition is commonly confused with voice recognition, speech recognition focuses on the translation of speech from a verbal format to a text one whereas voice recognition just seeks to identify an individual user’s voice.

IBM has had a prominent role within speech recognition since its inception, releasing of “Shoebox” in 1962. This machine had the ability to recognize 16 different words, advancing the initial work from Bell Labs from the 1950s. However, IBM didn’t stop there, but continued to innovate over the years, launching VoiceType Simply Speaking application in 1996. This speech recognition software had a 42,000-word vocabulary, supported English and Spanish, and included a spelling dictionary of 100,000 words.

While speech technology had a limited vocabulary in the early days, it is utilized in a wide number of industries today, such as automotive, technology, and healthcare. Its adoption has only continued to accelerate in recent years due to advancements in deep learning and big data.  Research  (link resides outside ibm.com) shows that this market is expected to be worth USD 24.9 billion by 2025.

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Many speech recognition applications and devices are available, but the more advanced solutions use AI and machine learning . They integrate grammar, syntax, structure, and composition of audio and voice signals to understand and process human speech. Ideally, they learn as they go — evolving responses with each interaction.

The best kind of systems also allow organizations to customize and adapt the technology to their specific requirements — everything from language and nuances of speech to brand recognition. For example:

• Language weighting: Improve precision by weighting specific words that are spoken frequently (such as product names or industry jargon), beyond terms already in the base vocabulary.
• Speaker labeling: Output a transcription that cites or tags each speaker’s contributions to a multi-participant conversation.
• Acoustics training: Attend to the acoustical side of the business. Train the system to adapt to an acoustic environment (like the ambient noise in a call center) and speaker styles (like voice pitch, volume and pace).
• Profanity filtering: Use filters to identify certain words or phrases and sanitize speech output.

Meanwhile, speech recognition continues to advance. Companies, like IBM, are making inroads in several areas, the better to improve human and machine interaction.

The vagaries of human speech have made development challenging. It’s considered to be one of the most complex areas of computer science – involving linguistics, mathematics and statistics. Speech recognizers are made up of a few components, such as the speech input, feature extraction, feature vectors, a decoder, and a word output. The decoder leverages acoustic models, a pronunciation dictionary, and language models to determine the appropriate output.

Speech recognition technology is evaluated on its accuracy rate, i.e. word error rate (WER), and speed. A number of factors can impact word error rate, such as pronunciation, accent, pitch, volume, and background noise. Reaching human parity – meaning an error rate on par with that of two humans speaking – has long been the goal of speech recognition systems. Research from Lippmann (link resides outside ibm.com) estimates the word error rate to be around 4 percent, but it’s been difficult to replicate the results from this paper.

Various algorithms and computation techniques are used to recognize speech into text and improve the accuracy of transcription. Below are brief explanations of some of the most commonly used methods:

• Natural language processing (NLP): While NLP isn’t necessarily a specific algorithm used in speech recognition, it is the area of artificial intelligence which focuses on the interaction between humans and machines through language through speech and text. Many mobile devices incorporate speech recognition into their systems to conduct voice search—e.g. Siri—or provide more accessibility around texting. 
• Hidden markov models (HMM): Hidden Markov Models build on the Markov chain model, which stipulates that the probability of a given state hinges on the current state, not its prior states. While a Markov chain model is useful for observable events, such as text inputs, hidden markov models allow us to incorporate hidden events, such as part-of-speech tags, into a probabilistic model. They are utilized as sequence models within speech recognition, assigning labels to each unit—i.e. words, syllables, sentences, etc.—in the sequence. These labels create a mapping with the provided input, allowing it to determine the most appropriate label sequence.
• N-grams: This is the simplest type of language model (LM), which assigns probabilities to sentences or phrases. An N-gram is sequence of N-words. For example, “order the pizza” is a trigram or 3-gram and “please order the pizza” is a 4-gram. Grammar and the probability of certain word sequences are used to improve recognition and accuracy.
• Neural networks: Primarily leveraged for deep learning algorithms, neural networks process training data by mimicking the interconnectivity of the human brain through layers of nodes. Each node is made up of inputs, weights, a bias (or threshold) and an output. If that output value exceeds a given threshold, it “fires” or activates the node, passing data to the next layer in the network. Neural networks learn this mapping function through supervised learning, adjusting based on the loss function through the process of gradient descent.  While neural networks tend to be more accurate and can accept more data, this comes at a performance efficiency cost as they tend to be slower to train compared to traditional language models.
• Speaker Diarization (SD): Speaker diarization algorithms identify and segment speech by speaker identity. This helps programs better distinguish individuals in a conversation and is frequently applied at call centers distinguishing customers and sales agents.

A wide number of industries are utilizing different applications of speech technology today, helping businesses and consumers save time and even lives. Some examples include:

Automotive: Speech recognizers improves driver safety by enabling voice-activated navigation systems and search capabilities in car radios.

Technology: Virtual agents are increasingly becoming integrated within our daily lives, particularly on our mobile devices. We use voice commands to access them through our smartphones, such as through Google Assistant or Apple’s Siri, for tasks, such as voice search, or through our speakers, via Amazon’s Alexa or Microsoft’s Cortana, to play music. They’ll only continue to integrate into the everyday products that we use, fueling the “Internet of Things” movement.

Healthcare: Doctors and nurses leverage dictation applications to capture and log patient diagnoses and treatment notes.

Sales: Speech recognition technology has a couple of applications in sales. It can help a call center transcribe thousands of phone calls between customers and agents to identify common call patterns and issues. AI chatbots can also talk to people via a webpage, answering common queries and solving basic requests without needing to wait for a contact center agent to be available. It both instances speech recognition systems help reduce time to resolution for consumer issues.

Security: As technology integrates into our daily lives, security protocols are an increasing priority. Voice-based authentication adds a viable level of security.

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Speech Recognition: Everything You Need to Know in 2024

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Speech recognition, also known as automatic speech recognition (ASR) , enables seamless communication between humans and machines. This technology empowers organizations to transform human speech into written text. Speech recognition technology can revolutionize many business applications , including customer service, healthcare, finance and sales.

In this comprehensive guide, we will explain speech recognition, exploring how it works, the algorithms involved, and the use cases of various industries.

If you require training data for your speech recognition system, here is a guide to finding the right speech data collection services.

What is speech recognition?

Speech recognition, also known as automatic speech recognition (ASR), speech-to-text (STT), and computer speech recognition, is a technology that enables a computer to recognize and convert spoken language into text.

Speech recognition technology uses AI and machine learning models to accurately identify and transcribe different accents, dialects, and speech patterns.

What are the features of speech recognition systems?

Speech recognition systems have several components that work together to understand and process human speech. Key features of effective speech recognition are:

• Audio preprocessing: After you have obtained the raw audio signal from an input device, you need to preprocess it to improve the quality of the speech input The main goal of audio preprocessing is to capture relevant speech data by removing any unwanted artifacts and reducing noise.
• Feature extraction: This stage converts the preprocessed audio signal into a more informative representation. This makes raw audio data more manageable for machine learning models in speech recognition systems.
• Language model weighting: Language weighting gives more weight to certain words and phrases, such as product references, in audio and voice signals. This makes those keywords more likely to be recognized in a subsequent speech by speech recognition systems.
• Acoustic modeling : It enables speech recognizers to capture and distinguish phonetic units within a speech signal. Acoustic models are trained on large datasets containing speech samples from a diverse set of speakers with different accents, speaking styles, and backgrounds.
• Speaker labeling: It enables speech recognition applications to determine the identities of multiple speakers in an audio recording. It assigns unique labels to each speaker in an audio recording, allowing the identification of which speaker was speaking at any given time.
• Profanity filtering: The process of removing offensive, inappropriate, or explicit words or phrases from audio data.

What are the different speech recognition algorithms?

Speech recognition uses various algorithms and computation techniques to convert spoken language into written language. The following are some of the most commonly used speech recognition methods:

• Hidden Markov Models (HMMs): Hidden Markov model is a statistical Markov model commonly used in traditional speech recognition systems. HMMs capture the relationship between the acoustic features and model the temporal dynamics of speech signals.
• Estimate the probability of word sequences in the recognized text
• Convert colloquial expressions and abbreviations in a spoken language into a standard written form
• Map phonetic units obtained from acoustic models to their corresponding words in the target language.
• Speaker Diarization (SD): Speaker diarization, or speaker labeling, is the process of identifying and attributing speech segments to their respective speakers (Figure 1). It allows for speaker-specific voice recognition and the identification of individuals in a conversation.

Figure 1: A flowchart illustrating the speaker diarization process

• Dynamic Time Warping (DTW): Speech recognition algorithms use Dynamic Time Warping (DTW) algorithm to find an optimal alignment between two sequences (Figure 2).

Figure 2: A speech recognizer using dynamic time warping to determine the optimal distance between elements

5. Deep neural networks: Neural networks process and transform input data by simulating the non-linear frequency perception of the human auditory system.

6. Connectionist Temporal Classification (CTC): It is a training objective introduced by Alex Graves in 2006. CTC is especially useful for sequence labeling tasks and end-to-end speech recognition systems. It allows the neural network to discover the relationship between input frames and align input frames with output labels.

Speech recognition vs voice recognition

Speech recognition is commonly confused with voice recognition, yet, they refer to distinct concepts. Speech recognition converts  spoken words into written text, focusing on identifying the words and sentences spoken by a user, regardless of the speaker’s identity. 

On the other hand, voice recognition is concerned with recognizing or verifying a speaker’s voice, aiming to determine the identity of an unknown speaker rather than focusing on understanding the content of the speech.

What are the challenges of speech recognition with solutions?

While speech recognition technology offers many benefits, it still faces a number of challenges that need to be addressed. Some of the main limitations of speech recognition include:

Acoustic Challenges:

• Assume a speech recognition model has been primarily trained on American English accents. If a speaker with a strong Scottish accent uses the system, they may encounter difficulties due to pronunciation differences. For example, the word “water” is pronounced differently in both accents. If the system is not familiar with this pronunciation, it may struggle to recognize the word “water.”

Solution: Addressing these challenges is crucial to enhancing  speech recognition applications’ accuracy. To overcome pronunciation variations, it is essential to expand the training data to include samples from speakers with diverse accents. This approach helps the system recognize and understand a broader range of speech patterns.

• For instance, you can use data augmentation techniques to reduce the impact of noise on audio data. Data augmentation helps train speech recognition models with noisy data to improve model accuracy in real-world environments.

Figure 3: Examples of a target sentence (“The clown had a funny face”) in the background noise of babble, car and rain.

Linguistic Challenges:

• Out-of-vocabulary words: Since the speech recognizers model has not been trained on OOV words, they may incorrectly recognize them as different or fail to transcribe them when encountering them.

Figure 4: An example of detecting OOV word

Solution: Word Error Rate (WER) is a common metric that is used to measure the accuracy of a speech recognition or machine translation system. The word error rate can be computed as:

Figure 5: Demonstrating how to calculate word error rate (WER)

• Homophones: Homophones are words that are pronounced identically but have different meanings, such as “to,” “too,” and “two”. Solution: Semantic analysis allows speech recognition programs to select the appropriate homophone based on its intended meaning in a given context. Addressing homophones improves the ability of the speech recognition process to understand and transcribe spoken words accurately.

Technical/System Challenges:

• Data privacy and security: Speech recognition systems involve processing and storing sensitive and personal information, such as financial information. An unauthorized party could use the captured information, leading to privacy breaches.

Solution: You can encrypt sensitive and personal audio information transmitted between the user’s device and the speech recognition software. Another technique for addressing data privacy and security in speech recognition systems is data masking. Data masking algorithms mask and replace sensitive speech data with structurally identical but acoustically different data.

Figure 6: An example of how data masking works

• Limited training data: Limited training data directly impacts  the performance of speech recognition software. With insufficient training data, the speech recognition model may struggle to generalize different accents or recognize less common words.

Solution: To improve the quality and quantity of training data, you can expand the existing dataset using data augmentation and synthetic data generation technologies.

13 speech recognition use cases and applications

In this section, we will explain how speech recognition revolutionizes the communication landscape across industries and changes the way businesses interact with machines.

Customer Service and Support

• Interactive Voice Response (IVR) systems: Interactive voice response (IVR) is a technology that automates the process of routing callers to the appropriate department. It understands customer queries and routes calls to the relevant departments. This reduces the call volume for contact centers and minimizes wait times. IVR systems address simple customer questions without human intervention by employing pre-recorded messages or text-to-speech technology . Automatic Speech Recognition (ASR) allows IVR systems to comprehend and respond to customer inquiries and complaints in real time.
• Customer support automation and chatbots: According to a survey, 78% of consumers interacted with a chatbot in 2022, but 80% of respondents said using chatbots increased their frustration level.
• Sentiment analysis and call monitoring: Speech recognition technology converts spoken content from a call into text. After  speech-to-text processing, natural language processing (NLP) techniques analyze the text and assign a sentiment score to the conversation, such as positive, negative, or neutral. By integrating speech recognition with sentiment analysis, organizations can address issues early on and gain valuable insights into customer preferences.
• Multilingual support: Speech recognition software can be trained in various languages to recognize and transcribe the language spoken by a user accurately. By integrating speech recognition technology into chatbots and Interactive Voice Response (IVR) systems, organizations can overcome language barriers and reach a global audience (Figure 7). Multilingual chatbots and IVR automatically detect the language spoken by a user and switch to the appropriate language model.

Figure 7: Showing how a multilingual chatbot recognizes words in another language

• Customer authentication with voice biometrics: Voice biometrics use speech recognition technologies to analyze a speaker’s voice and extract features such as accent and speed to verify their identity.

Sales and Marketing:

• Virtual sales assistants: Virtual sales assistants are AI-powered chatbots that assist customers with purchasing and communicate with them through voice interactions. Speech recognition allows virtual sales assistants to understand the intent behind spoken language and tailor their responses based on customer preferences.
• Transcription services : Speech recognition software records audio from sales calls and meetings and then converts the spoken words into written text using speech-to-text algorithms.

Automotive:

• Voice-activated controls: Voice-activated controls allow users to interact with devices and applications using voice commands. Drivers can operate features like climate control, phone calls, or navigation systems.
• Voice-assisted navigation: Voice-assisted navigation provides real-time voice-guided directions by utilizing the driver’s voice input for the destination. Drivers can request real-time traffic updates or search for nearby points of interest using voice commands without physical controls.

Healthcare:

• Recording the physician’s dictation
• Transcribing the audio recording into written text using speech recognition technology
• Editing the transcribed text for better accuracy and correcting errors as needed
• Formatting the document in accordance with legal and medical requirements.
• Virtual medical assistants: Virtual medical assistants (VMAs) use speech recognition, natural language processing, and machine learning algorithms to communicate with patients through voice or text. Speech recognition software allows VMAs to respond to voice commands, retrieve information from electronic health records (EHRs) and automate the medical transcription process.
• Electronic Health Records (EHR) integration: Healthcare professionals can use voice commands to navigate the EHR system , access patient data, and enter data into specific fields.

Technology:

• Virtual agents: Virtual agents utilize natural language processing (NLP) and speech recognition technologies to understand spoken language and convert it into text. Speech recognition enables virtual agents to process spoken language in real-time and respond promptly and accurately to user voice commands.

Further reading

• Top 5 Speech Recognition Data Collection Methods in 2023
• Top 11 Speech Recognition Applications in 2023

External Links

• 1. Databricks
• 2. PubMed Central
• 3. Qin, L. (2013). Learning Out-of-vocabulary Words in Automatic Speech Recognition . Carnegie Mellon University.
• 4. Wikipedia

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Speech Recognition

Speech recognition is the capability of an electronic device to understand spoken words. A microphone records a person's voice and the hardware converts the signal from analog sound waves to digital audio. The audio data is then processed by software , which interprets the sound as individual words.

A common type of speech recognition is "speech-to-text" or "dictation" software, such as Dragon Naturally Speaking, which outputs text as you speak. While you can buy speech recognition programs, modern versions of the Macintosh and Windows operating systems include a built-in dictation feature. This capability allows you to record text as well as perform basic system commands.

In Windows, some programs support speech recognition automatically while others do not. You can enable speech recognition for all applications by selecting All Programs → Accessories → Ease of Access → Windows Speech Recognition and clicking "Enable dictation everywhere." In OS X, you can enable dictation in the "Dictation & Speech" system preference pane. Simply check the "On" button next to Dictation to turn on the speech-to-text capability. To start dictating in a supported program, select Edit → Start Dictation . You can also view and edit spoken commands in OS X by opening the "Accessibility" system preference pane and selecting "Speakable Items."

Another type of speech recognition is interactive speech, which is common on mobile devices, such as smartphones and tablets . Both iOS and Android devices allow you to speak to your phone and receive a verbal response. The iOS version is called "Siri," and serves as a personal assistant. You can ask Siri to save a reminder on your phone, tell you the weather forecast, give you directions, or answer many other questions. This type of speech recognition is considered a natural user interface (or NUI ), since it responds naturally to your spoken input .

While many speech recognition systems only support English, some speech recognition software supports multiple languages. This requires a unique dictionary for each language and extra algorithms to understand and process different accents. Some dictation systems, such as Dragon Naturally Speaking, can be trained to understand your voice and will adapt over time to understand you more accurately.

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speech recognition

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• IBM Cloud Learn Hub - Speech Recognition
• University of Rochester - Department of Computer Science - An Overview of Speech Recognition

speech recognition , the ability of devices to respond to spoken commands. Speech recognition enables hands-free control of various devices and equipment (a particular boon to many disabled persons), provides input to automatic translation, and creates print-ready dictation. Among the earliest applications for speech recognition were automated telephone systems and medical dictation software . It is frequently used for dictation, for querying databases , and for giving commands to computer -based systems, especially in professions that rely on specialized vocabularies. It also enables personal assistants in vehicles and smartphones , such as Apple’s Siri.

Before any machine can interpret speech, a microphone must translate the vibrations of a person’s voice into a wavelike electrical signal. This signal in turn is converted by the system’s hardware —for instance, a computer’s sound card—into a digital signal. It is the digital signal that a speech recognition program analyzes in order to recognize separate phonemes , the basic building blocks of speech. The phonemes are then recombined into words. However, many words sound alike, and, in order to select the appropriate word, the program must rely on the context . Many programs establish context through trigram analysis, a method based on a database of frequent three-word clusters in which probabilities are assigned that any two words will be followed by a given third word. For example, if a speaker says “who am,” the next word will be recognized as the pronoun “I” rather than the similar-sounding but less likely “eye.” Nevertheless, human intervention is sometimes needed to correct errors.

Programs for recognizing a few isolated words, such as telephone voice navigation systems, work for almost every user. On the other hand, continuous speech programs, such as dictation programs, must be trained to recognize an individual’s speech patterns; training involves the user reading aloud samples of text. Today, with the growing power of personal computers and mobile devices, the accuracy of speech recognition has improved markedly. Error rates have been reduced to about 5 percent in vocabularies containing tens of thousands of words. Even greater accuracy is reached in limited vocabularies for specialized applications such as dictation of radiological diagnoses .

Speech Recognition

What Is Speech Recognition?

Speech recognition is the technology that allows a computer to recognize human speech and process it into text. It’s also known as automatic speech recognition ( ASR ), speech-to-text, or computer speech recognition. 

Speech recognition systems rely on technologies like artificial intelligence (AI) and machine learning (ML) to gain larger samples of speech, including different languages, accents, and dialects. AI is used to identify patterns of speech, words, and language to transcribe them into a written format.

In this blog post, we’ll take a deeper dive into speech recognition and look at how it works, its real-world applications, and how platforms like aiOla are using it to change the way we work.

Basic Speech Recognition Concepts

To start understanding speech recognition and all its applications, we need to first look at what it is and isn’t. While speech recognition is more than just the sum of its parts, it’s important to look at each of the parts that contribute to this technology to better grasp how it can make a real impact. Let’s take a look at some common concepts.

Speech Recognition vs. Speech Synthesis

Unlike speech recognition, which converts spoken language into a written format through a computer, speech synthesis does the same in reverse. In other words, speech synthesis is the creation of artificial speech derived from a written text, where a computer uses an AI-generated voice to simulate spoken language. For example, think of the language voice assistants like Siri or Alexa use to communicate information.

Phonetics and Phonology

Phonetics studies the physical sound of human speech, such as its acoustics and articulation. Alternatively, phonology looks at the abstract representation of sounds in a language including their patterns and how they’re organized. These two concepts need to be carefully weighed for speech AI algorithms to understand sound and language as a human might.

Acoustic Modeling

In acoustic modeling , the acoustic characteristics of audio and speech are looked at. When it comes to speech recognition systems, this process is essential since it helps analyze the audio features of each word, such as the frequency in which it’s used, the duration of a word, or the sounds it encompasses.

Language Modeling

Language modeling algorithms look at details like the likelihood of word sequences in a language. This type of modeling helps make speech recognition systems more accurate as it mimics real spoken language by looking at the probability of word combinations in phrases.

Speaker-Dependent vs. Speaker-Independent Systems

A system that’s dependent on a speaker is trained on the unique voice and speech patterns of a specific user, meaning the system might be highly accurate for that individual but not as much for other people. By contrast, a system that’s independent of a speaker can recognize speech for any number of speakers, and while more versatile, may be slightly less accurate.

How Does Speech Recognition Work?

There are a few different stages to speech recognition, each one providing another layer to how language is processed by a computer. Here are the different steps that make up the process.

• First, raw audio input undergoes a process called preprocessing , where background noise is removed to enhance sound quality and make recognition more manageable.
• Next, the audio goes through feature extraction , where algorithms identify distinct characteristics of sounds and words. 
• Then, these extracted features go through acoustic modeling , which as we described earlier, is the stage where acoustic and language models decide the most accurate visual representation of the word. These acoustic modeling systems are based on extensive datasets, allowing them to learn the acoustic patterns of different spoken words.
• At the same time, language modeling looks at the structure and probability of words in a sequence, which helps provide context. 
• After this, the output goes into a decoding sequence, where the speech recognition system matches data from the extracted features with the acoustic models. This helps determine the most likely word sequence.
• Finally, the audio and corresponding textual output go through post-processing , which refines the output by correcting errors and improving coherence to create a more accurate transcription.

When it comes to advanced systems, all of these stages are done nearly instantaneously, making this process almost invisible to the average user. All of these stages together have made speech recognition a highly versatile tool that can be used in many different ways, from virtual assistants to transcription services and beyond.

Types of Speech Recognition Systems

Speech recognition technology is used in many different ways today, transforming the way humans and machines interact and work together. From professional settings to helping us make our lives a little easier, this technology can take on many forms. Here are some of them.

Virtual Assistants

In 2022, 62% of US adults used a voice assistant on various mobile devices. Siri, Google Assistant, and Alexa are all examples of speech recognition in our daily lives. These applications respond to vocal commands and can interact with humans through natural language in order to complete tasks like sending messages, answering questions, or setting reminders.

Voice Search

Search engines like Google can be searched using voice instead of typing in a query, often with voice assistants. This allows users to conveniently search for a quick answer without sorting through content when they need to be hands-free, like when driving or multitasking. This technology has become so popular over the last few years that now 50% of US-based consumers use voice search every single day.

Transcription Services

Speech recognition has completely changed the transcription industry. It has enabled transcription services to automate the process of turning speech into text, increasing efficiency in many fields like education, legal services, healthcare, and even journalism.

Accessibility

With speech recognition, technologies that may have seemed out of reach are now accessible to people with disabilities. For example, for people with motor impairments or who are visually impaired, AI voice-to-text technology can help with the hands-free operation of things like keyboards, writing assistance for dictation, and voice commands to control devices.

Automotive Systems

Speech recognition is keeping drivers safer by giving them hands-free control over in-car features. Drivers can make calls, adjust the temperature, navigate, or even control the music without ever removing their hands from the wheel and instead just issuing voice commands to a speech-activated system.

How Does aiOla Use Speech Recognition?

aiOla’s AI-powered speech platform is revolutionizing the way certain industries work by bringing advanced speech recognition technology to companies in fields like aviation, fleet management, food safety, and manufacturing.

Traditionally, many processes in these industries were manual, forcing organizations to use a lot of time, budget, and resources to complete mission-critical tasks like inspections and maintenance. However, with aiOla’s advanced speech system, these otherwise labor and resource-intensive tasks can be reduced to a matter of minutes using natural language.

Rather than manually writing to record data during inspections, inspectors can speak about what they’re verifying and the data gets stored instantly. Similarly, through dissecting speech, aiOla can help with predictive maintenance of essential machinery, allowing food manufacturers to produce safer items and decrease downtime.

Since aiOla’s speech recognition platform understands over 100 languages and countless accents, dialects, and industry-specific jargon, the system is highly accurate and can help turn speech into action to go a step further and automate otherwise manual tasks.

Embracing Speech Recognition Technology

Looking ahead, we can only expect the technology that relies on speech recognition to improve and become more embedded into our day-to-day. Indeed, the market for this technology is expected to grow to $19.57 billion by 2030 . Whether it’s refining virtual assistants, improving voice search, or applying speech recognition to new industries, this technology is here to stay and enhance our personal and professional lives.

aiOla, while also a relatively new technology, is already making waves in industries like manufacturing, fleet management, and food safety. Through technological advancements in speech recognition, we only expect aiOla’s capabilities to continue to grow and support a larger variety of businesses and organizations.

Schedule a demo with one of our experts to see how aiOla’s AI speech recognition platform works in action.

What is speech recognition software? Speech recognition software is a technology that enables computers to convert speech into written words. This is done through algorithms that analyze audio signals along with AI, ML, and other technologies. What is a speech recognition example? A relatable example of speech recognition is asking a virtual assistant like Siri on a mobile device to check the day’s weather or set an alarm. While speech recognition can complete a lot more advanced tasks, this exemplifies how this technology is commonly used in everyday life. What is speech recognition in AI? Speech recognition in AI refers to how artificial intelligence processes are used to aid in recognizing voice and language using advanced models and algorithms trained on vast amounts of data. What are some different types of speech recognition? A few different types of speech recognition include speaker-dependent and speaker-independent systems, command and control systems, and continuous speech recognition. What is the difference between voice recognition and speech recognition? Speech recognition converts spoken language into text, while voice recognition works to identify a speaker’s unique vocal characteristics for authentication purposes. In essence, voice recognition is more tied to identity rather than transcription.

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How Does Speech Recognition Work? (9 Simple Questions Answered)

• by Team Experts
• July 2, 2023 July 3, 2023

Discover the Surprising Science Behind Speech Recognition – Learn How It Works in 9 Simple Questions!

Speech recognition is the process of converting spoken words into written or machine-readable text. It is achieved through a combination of natural language processing , audio inputs, machine learning , and voice recognition . Speech recognition systems analyze speech patterns to identify phonemes , the basic units of sound in a language. Acoustic modeling is used to match the phonemes to words , and word prediction algorithms are used to determine the most likely words based on context analysis . Finally, the words are converted into text.

What is Natural Language Processing and How Does it Relate to Speech Recognition?

How do audio inputs enable speech recognition, what role does machine learning play in speech recognition, how does voice recognition work, what are the different types of speech patterns used for speech recognition, how is acoustic modeling used for accurate phoneme detection in speech recognition systems, what is word prediction and why is it important for effective speech recognition technology, how can context analysis improve accuracy of automatic speech recognition systems, common mistakes and misconceptions.

Natural language processing (NLP) is a branch of artificial intelligence that deals with the analysis and understanding of human language. It is used to enable machines to interpret and process natural language, such as speech, text, and other forms of communication . NLP is used in a variety of applications , including automated speech recognition , voice recognition technology , language models, text analysis , text-to-speech synthesis , natural language understanding , natural language generation, semantic analysis , syntactic analysis, pragmatic analysis, sentiment analysis, and speech-to-text conversion. NLP is closely related to speech recognition , as it is used to interpret and understand spoken language in order to convert it into text.

Audio inputs enable speech recognition by providing digital audio recordings of spoken words . These recordings are then analyzed to extract acoustic features of speech, such as pitch, frequency, and amplitude. Feature extraction techniques , such as spectral analysis of sound waves, are used to identify and classify phonemes . Natural language processing (NLP) and machine learning models are then used to interpret the audio recordings and recognize speech. Neural networks and deep learning architectures are used to further improve the accuracy of voice recognition . Finally, Automatic Speech Recognition (ASR) systems are used to convert the speech into text, and noise reduction techniques and voice biometrics are used to improve accuracy .

Machine learning plays a key role in speech recognition , as it is used to develop algorithms that can interpret and understand spoken language. Natural language processing , pattern recognition techniques , artificial intelligence , neural networks, acoustic modeling , language models, statistical methods , feature extraction , hidden Markov models (HMMs), deep learning architectures , voice recognition systems, speech synthesis , and automatic speech recognition (ASR) are all used to create machine learning models that can accurately interpret and understand spoken language. Natural language understanding is also used to further refine the accuracy of the machine learning models .

Voice recognition works by using machine learning algorithms to analyze the acoustic properties of a person’s voice. This includes using voice recognition software to identify phonemes , speaker identification, text normalization , language models, noise cancellation techniques , prosody analysis , contextual understanding , artificial neural networks, voice biometrics , speech synthesis , and deep learning . The data collected is then used to create a voice profile that can be used to identify the speaker .

The different types of speech patterns used for speech recognition include prosody , contextual speech recognition , speaker adaptation , language models, hidden Markov models (HMMs), neural networks, Gaussian mixture models (GMMs) , discrete wavelet transform (DWT), Mel-frequency cepstral coefficients (MFCCs), vector quantization (VQ), dynamic time warping (DTW), continuous density hidden Markov model (CDHMM), support vector machines (SVM), and deep learning .

Acoustic modeling is used for accurate phoneme detection in speech recognition systems by utilizing statistical models such as Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs). Feature extraction techniques such as Mel-frequency cepstral coefficients (MFCCs) are used to extract relevant features from the audio signal . Context-dependent models are also used to improve accuracy . Discriminative training techniques such as maximum likelihood estimation and the Viterbi algorithm are used to train the models. In recent years, neural networks and deep learning algorithms have been used to improve accuracy , as well as natural language processing techniques .

Word prediction is a feature of natural language processing and artificial intelligence that uses machine learning algorithms to predict the next word or phrase a user is likely to type or say. It is used in automated speech recognition systems to improve the accuracy of the system by reducing the amount of user effort and time spent typing or speaking words. Word prediction also enhances the user experience by providing faster response times and increased efficiency in data entry tasks. Additionally, it reduces errors due to incorrect spelling or grammar, and improves the understanding of natural language by machines. By using word prediction, speech recognition technology can be more effective , providing improved accuracy and enhanced ability for machines to interpret human speech.

Context analysis can improve the accuracy of automatic speech recognition systems by utilizing language models, acoustic models, statistical methods , and machine learning algorithms to analyze the semantic , syntactic, and pragmatic aspects of speech. This analysis can include word – level , sentence- level , and discourse-level context, as well as utterance understanding and ambiguity resolution. By taking into account the context of the speech, the accuracy of the automatic speech recognition system can be improved.

• Misconception : Speech recognition requires a person to speak in a robotic , monotone voice. Correct Viewpoint: Speech recognition technology is designed to recognize natural speech patterns and does not require users to speak in any particular way.
• Misconception : Speech recognition can understand all languages equally well. Correct Viewpoint: Different speech recognition systems are designed for different languages and dialects, so the accuracy of the system will vary depending on which language it is programmed for.
• Misconception: Speech recognition only works with pre-programmed commands or phrases . Correct Viewpoint: Modern speech recognition systems are capable of understanding conversational language as well as specific commands or phrases that have been programmed into them by developers.

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What is Speech Recognition?

Speech recognition or speech-to-text recognition , is the capacity of a machine or program to recognize spoken words and transform them into text. Speech Recognition  is an important feature in several applications used such as home automation, artificial intelligence, etc. In this article, we are going to explore how speech recognition software work, speech recognition algorithms, and the role of NLP. See examples of how this technology is used in everyday life and various industries, making interactions with devices smarter and more intuitive.

Speech Recognition , also known as automatic speech recognition ( ASR ), computer speech recognition, or speech-to-text, focuses on enabling computers to understand and interpret human speech. Speech recognition involves converting spoken language into text or executing commands based on the recognized words. This technology relies on sophisticated algorithms and machine learning models to process and understand human speech in real-time , despite the variations in accents , pitch , speed , and slang .

Key Features of Speech Recognition

• Accuracy and Speed: They can process speech in real-time or near real-time, providing quick responses to user inputs.
• Natural Language Understanding (NLU): NLU enables systems to handle complex commands and queries , making technology more intuitive and user-friendly .
• Multi-Language Support: Support for multiple languages and dialects , allowing users from different linguistic backgrounds to interact with technology in their native language.
• Background Noise Handling: This feature is crucial for voice-activated systems used in public or outdoor settings.

Speech Recognition Algorithms

Speech recognition technology relies on complex algorithms to translate spoken language into text or commands that computers can understand and act upon. Here are the algorithms and approaches used in speech recognition:

1. Hidden Markov Models (HMM)

Hidden Markov Models have been the backbone of speech recognition for many years. They model speech as a sequence of states, with each state representing a phoneme (basic unit of sound) or group of phonemes. HMMs are used to estimate the probability of a given sequence of sounds, making it possible to determine the most likely words spoken. Usage : Although newer methods have surpassed HMM in performance, it remains a fundamental concept in speech recognition, often used in combination with other techniques.

2. Natural language processing (NLP)

NLP is the area of  artificial intelligence  which focuses on the interaction between humans and machines through language through speech and text. Many mobile devices incorporate speech recognition into their systems to conduct voice search. Example such as : Siri or provide more accessibility around texting. 

3. Deep Neural Networks (DNN)

DNNs have improved speech recognition’s accuracy a lot. These networks can learn hierarchical representations of data, making them particularly effective at modeling complex patterns like those found in human speech. DNNs are used both for acoustic modeling , to better understand the sound of speech , and for language modeling, to predict the likelihood of certain word sequences.

4. End-to-End Deep Learning

Now, the trend has shifted towards end-to-end deep learning models , which can directly map speech inputs to text outputs without the need for intermediate phonetic representations. These models, often based on advanced RNNs , Transformers, or Attention Mechanisms , can learn more complex patterns and dependencies in the speech signal.

What is Automatic Speech Recognition?

Automatic Speech Recognition (ASR) is a technology that enables computers to understand and transcribe spoken language into text. It works by analyzing audio input, such as spoken words, and converting them into written text , typically in real-time. ASR systems use algorithms and machine learning techniques to recognize and interpret speech patterns , phonemes, and language models to accurately transcribe spoken words. This technology is widely used in various applications, including virtual assistants , voice-controlled devices , dictation software , customer service automation , and language translation services .

What is Dragon speech recognition software?

Dragon speech recognition software is a program developed by Nuance Communications that allows users to dictate text and control their computer using voice commands. It transcribes spoken words into written text in real-time , enabling hands-free operation of computers and devices. Dragon software is widely used for various purposes, including dictating documents , composing emails , navigating the web , and controlling applications . It also features advanced capabilities such as voice commands for editing and formatting text , as well as custom vocabulary and voice profiles for improved accuracy and personalization.

What is a normal speech recognition threshold?

The normal speech recognition threshold refers to the level of sound, typically measured in decibels (dB) , at which a person can accurately recognize speech. In quiet environments, this threshold is typically around 0 to 10 dB for individuals with normal hearing. However, in noisy environments or for individuals with hearing impairments , the threshold may be higher, meaning they require a louder volume to accurately recognize speech .

Uses of Speech Recognition

• Virtual Assistants: These are like digital helpers that understand what you say. They can do things like set reminders, search the internet, and control smart home devices, all without you having to touch anything. Examples include Siri , Alexa , and Google Assistant .
• Accessibility Tools: Speech recognition makes technology easier to use for people with disabilities. Features like voice control on phones and computers help them interact with devices more easily. There are also special apps for people with disabilities.
• Automotive Systems: In cars, you can use your voice to control things like navigation and music. This helps drivers stay focused and safe on the road. Examples include voice-activated navigation systems in cars.
• Healthcare: Doctors use speech recognition to quickly write down notes about patients, so they have more time to spend with them. There are also voice-controlled bots that help with patient care. For example, doctors use dictation tools to write down patient information quickly.
• Customer Service: Speech recognition is used to direct customer calls to the right place or provide automated help. This makes things run smoother and keeps customers happy. Examples include call centers that you can talk to and customer service bots .
• Education and E-Learning: Speech recognition helps people learn languages by giving them feedback on their pronunciation. It also transcribes lectures, making them easier to understand. Examples include language learning apps and lecture transcribing services.
• Security and Authentication: Voice recognition, combined with biometrics , keeps things secure by making sure it’s really you accessing your stuff. This is used in banking and for secure facilities. For example, some banks use your voice to make sure it’s really you logging in.
• Entertainment and Media: Voice recognition helps you find stuff to watch or listen to by just talking. This makes it easier to use things like TV and music services . There are also games you can play using just your voice.

Speech recognition is a powerful technology that lets computers understand and process human speech. It’s used everywhere, from asking your smartphone for directions to controlling your smart home devices with just your voice. This tech makes life easier by helping with tasks without needing to type or press buttons, making gadgets like virtual assistants more helpful. It’s also super important for making tech accessible to everyone, including those who might have a hard time using keyboards or screens. As we keep finding new ways to use speech recognition, it’s becoming a big part of our daily tech life, showing just how much we can do when we talk to our devices.

What is Speech Recognition?- FAQs

What are examples of speech recognition.

Note Taking/Writing: An example of speech recognition technology in use is speech-to-text platforms such as Speechmatics or Google’s speech-to-text engine. In addition, many voice assistants offer speech-to-text translation.

Is speech recognition secure?

Security concerns related to speech recognition primarily involve the privacy and protection of audio data collected and processed by speech recognition systems. Ensuring secure data transmission, storage, and processing is essential to address these concerns.

Is speech recognition and voice recognition same?

No, speech recognition and voice recognition are different. Speech recognition converts spoken words into text using NLP, focusing on the content of speech. Voice recognition, however, identifies the speaker based on vocal characteristics, emphasizing security and personalization without interpreting the speech’s content.

What is speech recognition in AI?

Speech recognition is the process of converting sound signals to text transcriptions. Steps involved in conversion of a sound wave to text transcription in a speech recognition system are: Recording: Audio is recorded using a voice recorder. Sampling: Continuous audio wave is converted to discrete values.

What are the type of Speech Recognition?

Dictation Systems: Convert speech to text. Voice Command Systems: Execute spoken commands. Speaker-Dependent Systems: Trained for specific users. Speaker-Independent Systems: Work for any user. Continuous Speech Recognition: Allows natural, flowing speech. Discrete Speech Recognition: Requires pauses between words. NLP-Integrated Systems: Understand context and meaning

How accurate is speech recognition technology?

The accuracy of speech recognition technology can vary depending on factors such as the quality of audio input , language complexity , and the specific application or system being used. Advances in machine learning and deep learning have improved accuracy significantly in recent years.

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Speech Recognition

Speech Recognition is the decoding of human speech into transcribed text through a computer program. To recognize spoken words, the program must transcribe the incoming sound signal into a digitized representation, which must then be compared to an enormous database of digitized representations of spoken words. To transcribe speech with any tolerable degree of accuracy, users must speak each word independently, with a pause between each word and this substantially slows the speed of speech-recognition systems and calls their utility into question, With the exception in the case of physical disabilities that would prevent input by other means. See discrete speech recognition.

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What Is Speech Recognition and How Does It Work?

With modern devices, you can check the weather, place an order, make a call, and play your favorite song entirely hands-free. Giving voice commands to your gadgets makes it incredibly easy to multitask and handle daily chores. It’s all possible thanks to speech recognition technology.

Let’s explore speech recognition further to understand how it has evolved, how it works, and where it’s used today.

What Is Speech Recognition?

Speech recognition is the capacity of a computer to convert human speech into written text. Also known as automatic/automated speech recognition (ASR) and speech to text (STT), it’s a subfield of computer science and computational linguistics. Today, this technology has evolved to the point where machines can understand natural speech in different languages, dialects, accents, and speech patterns.

Speech Recognition vs. Voice Recognition

Although similar, speech and voice recognition are not the same technology. Here’s a breakdown below.

Speech recognition aims to identify spoken words and turn them into written text, in contrast to voice recognition which identifies an individual’s voice. Essentially, voice recognition recognizes the speaker, while speech recognition recognizes the words that have been spoken. Voice recognition is often used for security reasons, such as voice biometrics. And speech recognition is implemented to identify spoken words, regardless of who the speaker is.

History of Speech Recognition

You might be surprised that the first speech recognition technology was created in the 1950s. Browsing through the history of the technology gives us interesting insights into how it has evolved, gradually increasing vocabulary size and processing speed.

1952: The first speech recognition software was “Audrey,” developed by Bell Labs, which could recognize spoken numbers from 0 to 9.

1960s: At the Radio Research Lab in Tokyo, Suzuki and Nakata built a machine able to recognize vowels.

1962: The next breakthrough was IBM’s “Shoebox,” which could identify 16 different words.

1976: The “ Harpy ” speech recognition system at Carnegie-Mellon University could understand over 1,000 words.

Mid-1980s: Fred Jelinek's research team developed a voice-activated typewriter, Tangora, with an expanded bandwidth of 20,000 words.

1992: Developed at Bell Labs, AT&T’s Voice Recognition Call Processing service was able to route phone calls without a human operator.

2007: Google started working on its first speech recognition software, which led to the creation of Google Voice Search in 2012.

2010s: Apple’s Siri and Amazon Alexa came into the scene, making speech recognition software easily available to the masses. 

How Does Speech Recognition Work?

We’re used to the simplicity of operating a gadget through voice, but we’re usually unaware of the complex processes taking place behind the scenes.

Speech recognition systems incorporate linguistics, mathematics, deep learning, and statistics to process spoken language. The software uses statistical models or neural networks to convert the speech input into word output. The role of natural language processing (NLP) is also significant, as it’s implemented to return relevant text to the given voice command.

Computers go through the following steps to interpret human speech:

• The microphone translates sound vibrations into electrical signals.
• The computer then digitizes the received signals.
• Speech recognition software analyzes digital signals to identify sounds and distinguish phonemes (the smallest units of speech).
• Algorithms match the signals with suitable text that represents the sounds.

This process gets more complicated when you account for background noise, context, accents, slang, cross talk, and other influencing factors. With the application of artificial intelligence and  machine learning , speech recognition technology processes voice interactions to improve performance and precision over time.

Speech Recognition Key Features

Here are the key features that enable speech recognition systems to function:

• Language weighting: This feature gives weight to certain words and phrases over others to better respond in a given context. For instance, you can train the software to pay attention to industry or product-specific words.
• Speaker labeling: It labels all speakers in a group conversation to note their individual contributions.
• Profanity filtering: Recognizes and filters inappropriate words to disallow unwanted language.
• Acoustics training: Distinguishes ambient noise, speaker style, pace, and volume to tune out distractions. This feature comes in handy in busy call centers and office spaces.

Speech Recognition Benefits

Speech recognition has various advantages to offer to businesses and individuals alike. Below are just a few of them. 

Faster Communication

Communicating through voice rather than typing every individual letter speeds up the process significantly. This is true both for interpersonal and human-to-machine communication. Think about how often you turn to your phone assistant to send a text message or make a call.

Multitasking

Completing actions hands-free gives us the opportunity to handle multiple tasks at once, which is a huge benefit in our busy, fast-paced lives. Voice search , for example, allows us to look up information anytime, anywhere, and even have the assistant read out the text for us.

Aid for Hearing and Visual Impairments

Speech-to-text and text-to-speech systems are of substantial importance to people with visual impairments. Similarly, users with hearing difficulties rely on audio transcription software to understand speech. Tools like Google Meet can even provide captions in different languages by translating the speech in real-time.

Real-Life Applications of Speech Recognition

The practical applications of speech recognition span various industries and areas of life. Speech recognition has become prominent both in personal and business use.

• Technology: Mobile assistants, smart home devices, and self-driving cars have ceased to be sci-fi fantasies thanks to the advancement of speech recognition technology. Apple, Google, Microsoft, Amazon, and many others have succeeded in building powerful software that’s now closely integrated into our daily lives.
• Education: The easy conversion between verbal and written language aids students in learning information in their preferred format. Speech recognition assists with many academic tasks, from planning and completing assignments to practicing new languages. 
• Customer Service:  Virtual assistants capable of speech recognition can process spoken queries from customers and identify the intent. Hoory is an example of an assistant that converts speech to text and vice versa to listen to user questions and read responses out loud.

Speech Recognition Summarized

Speech recognition allows us to operate and communicate with machines through voice. Behind the scenes, there are complex speech recognition algorithms that enable such interactions. As the algorithms become more sophisticated, we get better software that recognizes various speech patterns, dialects, and even languages.

Faster communication, hands-free operations, and hearing/visual impairment aid are some of the technology's biggest impacts. But there’s much more to expect from speech-activated software, considering the incredible rate at which it keeps growing.

Speech Recognition: Definition, Importance and Uses

Transkriptor 2024-01-17

Speech recognition, known as voice recognition or speech-to-text, is a technological development that converts spoken language into written text. It has two main benefits, these include enhancing task efficiency and increasing accessibility for everyone including individuals with physical impairments.

The alternative of speech recognition is manual transcription. Manual transcription is the process of converting spoken language into written text by listening to an audio or video recording and typing out the content.

There are many speech recognition software, but a few names stand out in the market when it comes to speech recognition software; Dragon NaturallySpeaking, Google's Speech-to-Text and Transkriptor.

The concept behind "what is speech recognition?" pertains to the capacity of a system or software to understand and transform oral communication into written textual form. It functions as the fundamental basis for a wide range of modern applications, ranging from voice-activated virtual assistants such as Siri or Alexa to dictation tools and hands-free gadget manipulation.

The development is going to contribute to a greater integration of voice-based interactions into an individual's everyday life.

What is Speech Recognition?

Speech recognition, known as ASR, voice recognition or speech-to-text, is a technological process. It allows computers to analyze and transcribe human speech into text.

How does Speech Recognition work?

Speech recognition technology works similar to how a person has a conversation with a friend. Ears detect the voice, and the brain processes and understands.The technology does, but it involves advanced software as well as intricate algorithms. There are four steps to how it works.

The microphone records the sounds of the voice and converts them into little digital signals when users speak into a device. The software processes the signals to exclude other voices and enhance the primary speech. The system breaks down the speech into small units called phonemes.

Different phonemes give their own unique mathematical representations by the system. It is able to differentiate between individual words and make educated predictions about what the speaker is trying to convey.

The system uses a language model to predict the right words. The model predicts and corrects word sequences based on the context of the speech.

The textual representation of the speech is produced by the system. The process requires a short amount of time. However, the correctness of the transcription is contingent on a variety of circumstances including the quality of the audio.

What is the importance of Speech Recognition?

The importance of speech recognition is listed below.

• Efficiency: It allows for hands-free operation. It makes multitasking easier and more efficient.
• Accessibility: It provides essential support for people with disabilities.
• Safety: It reduces distractions by allowing hands-free phone calls.
• Real-time translation: It facilitates real-time language translation. It breaks down communication barriers.
• Automation: It powers virtual assistants like Siri, Alexa, and Google Assistant, streamlining many daily tasks.
• Personalization: It allows devices and apps to understand user preferences and commands.

What are the Uses of Speech Recognition?

The 7 uses of speech recognition are listed below.

• Virtual Assistants. It includes powering voice-activated assistants like Siri, Alexa, and Google Assistant.
• Transcription services. It involves converting spoken content into written text for documentation, subtitles, or other purposes.
• Healthcare. It allows doctors and nurses to dictate patient notes and records hands-free.
• Automotive. It covers enabling voice-activated controls in vehicles, from playing music to navigation.
• Customer service. It embraces powering voice-activated IVRs in call centers.
• Educatio.: It is for easing in language learning apps, aiding in pronunciation, and comprehension exercises.
• Gaming. It includes providing voice command capabilities in video games for a more immersive experience.

Who Uses Speech Recognition?

General consumers, professionals, students, developers, and content creators use voice recognition software. Voice recognition sends text messages, makes phone calls, and manages their devices with voice commands. Lawyers, doctors, and journalists are among the professionals who employ speech recognition. Using speech recognition software, they dictate domain-specific information.

What is the Advantage of Using Speech Recognition?

The advantage of using speech recognition is mainly its accessibility and efficiency. It makes human-machine interaction more accessible and efficient. It reduces the human need which is also time-consuming and open to mistakes.

It is beneficial for accessibility. People with hearing difficulties use voice commands to communicate easily. Healthcare has seen considerable efficiency increases, with professionals using speech recognition for quick recording. Voice commands in driving settings help maintain safety and allow hands and eyes to focus on essential duties.

What is the Disadvantage of Using Speech Recognition?

The disadvantage of using speech recognition is its potential for inaccuracies and its reliance on specific conditions. Ambient noise or  accents confuse the algorithm. It results in misinterpretations or transcribing errors.

These inaccuracies are problematic. They are crucial in sensitive situations such as medical transcribing or legal documentation. Some systems need time to learn how a person speaks in order to work correctly. Voice recognition systems probably have difficulty interpreting multiple speakers at the same time. Another disadvantage is privacy. Voice-activated devices may inadvertently record private conversations.

What are the Different Types of Speech Recognition?

The 3 different types of speech recognition are listed below.

• Automatic Speech Recognition (ASR)
• Speaker-Dependent Recognition (SDR)
• Speaker-Independent Recognition (SIR)

Automatic Speech Recognition (ASR) is one of the most common types of speech recognition . ASR systems convert spoken language into text format. Many applications use them like Siri and Alexa. ASR focuses on understanding and transcribing speech regardless of the speaker, making it widely applicable.

Speaker-Dependent recognition recognizes a single user's voice. It needs time to learn and adapt to their particular voice patterns and accents. Speaker-dependent systems are very accurate because of the training. However, they struggle to recognize new voices.

Speaker-independent recognition interprets and transcribes speech from any speaker. It does not care about the accent, speaking pace, or voice pitch. These systems are useful in applications with many users.

What Accents and Languages Can Speech Recognition Systems Recognize?

The accents and languages that speech recognition systems can recognize are English, Spanish, and Mandarin to less common ones. These systems frequently incorporate customized models for distinguishing dialects and accents. It recognizes the diversity within languages. Transkriptor, for example, as a dictation software, supports over 100 languages.

Is Speech Recognition Software Accurate?

Yes, speech recognition software is accurate above 95%. However, its accuracy varies depending on a number of things. Background noise and audio quality are two examples of these.

How Accurate Can the Results of Speech Recognition Be?

Speech recognition results can achieve accuracy levels of up to 99% under optimal conditions. The highest level of speech recognition accuracy requires controlled conditions such as audio quality and background noises. Leading speech recognition systems have reported accuracy rates that exceed 99%.

How Does Text Transcription Work with Speech Recognition?

Text transcription works with speech recognition by analyzing and processing audio signals. Text transcription process starts with a microphone that records the speech and converts it to digital data. The algorithm then divides the digital sound into small pieces and analyzes each one to identify its distinct tones.

Advanced computer algorithms aid the system for matching these sounds to recognized speech patterns. The software compares these patterns to a massive language database to find the words users articulated. It then brings the words together to create a logical text.

How are Audio Data Processed with Speech Recognition?

Speech recognition processes audio data by splitting sound waves, extracting features, and mapping them to linguistic parts. The system collects and processes continuous sound waves when users speak into a device. The software advances to the feature extraction stage.

The software isolates specific features of the sound. It focuses on phonemes that are crucial for identifying one phoneme from another. The process entails evaluating the frequency components.

The system then starts using its trained models. The software combines the extracted features to known phonemes by using vast databases and machine learning models.

The system takes the phonemes, and puts them together to form words and phrases. The system combines technology skills and language understanding to convert noises into intelligible text or commands.

What is the best speech recognition software?

The 3 best speech recognition software are listed below.

Transkriptor

• Dragon NaturallySpeaking
• Google's Speech-to-Text

However, choosing the best speech recognition software depends on personal preferences.

Transkriptor is an online transcription software that uses artificial intelligence for quick and accurate transcription. Users are able to translate their transcripts with a single click right from the Transkriptor dashboard. Transkriptor technology is available in the form of a smartphone app, a Google Chrome extension, and a virtual meeting bot. It is compatible with popular platforms like Zoom, Microsoft Teams, and Google Meet which makes it one of the Best Speech Recognition Software.

Dragon NaturallySpeaking allows users to transform spoken speech into written text. It offers accessibility as well as adaptations for specific linguistic languages. Users like software’s adaptability for different vocabularies.

Google's Speech-to-Text is widely used for its scalability, integration options, and ability to support multiple languages. Individuals use it in a variety of applications ranging from transcription services to voice-command systems.

Is Speech Recognition and Dictation the Same?

No, speech recognition and dictation are not the same. Their principal goals are different, even though both voice recognition and dictation make conversion of spoken language into text. Speech recognition is a broader term covering the technology's ability to recognize and analyze spoken words. It converts them into a format that computers understand.

Dictation refers to the process of speaking aloud for recording. Dictation software uses speech recognition to convert spoken words into written text.

What is the Difference between Speech Recognition and Dictation?

The difference between speech recognition and dictation are related to their primary purpose, interactions, and scope. Itss primary purpose is to recognize and understand spoken words. Dictation has a more definite purpose. It focuses on directly transcribing spoken speech into written form.

Speech Recognition covers a wide range of applications in terms of scope. It helps voice assistants respond to user questions. Dictation has a narrower scope.

It provides a more dynamic interactive experience, often allowing for two-way dialogues. For example, virtual assistants such as Siri or Alexa not only understand user requests but also provide feedback or answers. Dictation works in a more basic fashion. It's typically a one-way procedure in which the user speaks and the system transcribes without the program engaging in a response discussion.

Frequently Asked Questions

Transkriptor stands out for its ability to support over 100 languages and its ease of use across various platforms. Its AI-driven technology focuses on quick and accurate transcription.

Yes, modern speech recognition software is increasingly adept at handling various accents. Advanced systems use extensive language models that include different dialects and accents, allowing them to accurately recognize and transcribe speech from diverse speakers.

Speech recognition technology greatly enhances accessibility by enabling voice-based control and communication, which is particularly beneficial for individuals with physical impairments or motor skill limitations. It allows them to operate devices, access information, and communicate effectively.

Speech recognition technology's efficiency in noisy environments has improved, but it can still be challenging. Advanced systems employ noise cancellation and voice isolation techniques to filter out background noise and focus on the speaker's voice.

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• Speech Recognition

This learning resource is about automatic conversion of spoken language into text, that can be stored as documents or processed as commands to control devices e.g. for handicapped people or elderly people or in a commercial setting allows to order goods and services by audio commands. The learning resource is based on the Open Community Approach so the used tools are Open Source to assure that learner have access to the tools.

• 1 Learning Tasks
• 2 Definition
• 3 Training of Speech Recognition Algorithms
• 4 Applications
• 5 Models, methods, and algorithms
• 6.1 Usage in education and daily life
• 6.2 Further applications
• 7.1 Conferences and journals
• 7.3 Software
• 9 References
• 10 Further reading
• 11 External links
• 12 Page Information

Learning Tasks

• (Applications of Speech Recognition) Analyse the possible applications of speech recognition and identify challenges of the application!
• (Human Speech Recognition) Compare human comprehension of speech with the algorithmic speech recognition approach. What are the similarities and differences of human and algorithmic speech recognition?
• What are similarities and difference between text and emotion recognition in speech analysis?
• What are possible application areas in digital assitants for both speech recognition and emotion recognition?
• Analyze the different types of information systems and identify different areas of application of speech recognition and include mobile devices in your consideration!
• (History) Analyse the history of speech recognition and compare the steps of development with current applications. Identify the major steps that are required for the current applications of speech recognition!
• ( Risk Literacy ) Identify possible areas of risks and possible risk mitigation strategies if speech recognition is implemented in mobile devices, or with voice control for Internet of Things in general? What are required capacity building measures for business, research and development!
• ( Commercial Data Harvesting ) Apply the concept of speech recognition to commercial data harvesting . What are potential benefits for generation of tailored advertisments for the users according to their generated profile? How is speech recognition contributing to user profile? What is the difference between offline and online speech recognition systems due to submission of recognized text or audio files submitted to remote servers for speech recognition?
• (Context Awareness of Speech Recognition) The word "Fire" with a candle in your hand and with burning house in the background creates a different context and different expectations of people listening to what someone is going to tell you. Exlain why context awareness can be helpful to optimize the recognition correctness? How can a speech recognition system detect a context to the speech recognition. I.e. detecting the context without a user setting that switches to a dictation mode e.g. for medical report for X-Ray images.
• ( Audio-Video-Compression ) Go to the learning resource about Audio-Video-Compression and explain how Speech Recognition can be used in conjunction with Speech Synthesis to reduce the consumption of bandwidth for Video conferencing .
• ( Performance ) Explain why the performance of speech recognition and accurancy is relevant in many applications. Discuss application in cars or in general in vehicles. Which voice commands can be applied in a traffic situation and which command (not accurately recognized) could cause trouble or even an accident for the driver. Order the theortical application of speech recognition (e.g. "turn right at crossing", "switch on/off music",...) in terms of required performance and accuracy resp. to current available technologies to perform the command in an acceptable way.
• Explain how the recognized words are encoded for speech recognition in the demo application (digits, cities, operating systems).
• Explain how the concept of speech recognition can support handicapped people [1] with navigating in a WebApp or offline AppLSAC for digital learning environments .
• (Size of Vocabulary) Explain how the size of the recognized vocabulary determines the precision of recognition.
• (People with Disabilities) [2] Explore the available frameworks Open Source offline infrastructure for speech recognition without sending audio streams to a remote server for processing. Identify options to control robots or in the context of Ambient Assisted Living with voice recognition [3] .
• Collaborative development of the Open Source code base of the speech recognition infrastructure,
• Application on the collaborative development of a domain specific vocabulary for speech recognition for specific application scenarios.
• Application on Open Educational Resources that support learners in using speech recognition and Open Source developers in integrating Open Source frameworks into learning environments.

Speech recognition is the interdisciplinary subfield of computational linguistics that develops methodologies and technologies that enables the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition ( ASR ), computer speech recognition or speech to text ( STT ). It incorporates knowledge and research in the linguistics , computer science , and electrical engineering fields.

Training of Speech Recognition Algorithms

Some speech recognition systems require "training" (also called "enrollment") where an individual speaker reads text or isolated vocabulary into the system. The system analyzes the person's specific voice and uses it to fine-tune the recognition of that person's speech, resulting in increased accuracy. Systems that do not use training are called "speaker independent" [4] systems. Systems that use training are called "speaker dependent".

Applications

Speech recognition applications include voice user interfaces such as voice dialing (e.g. "call home"), call routing (e.g. "I would like to make a collect call"), domotic appliance control, search (e.g. find a podcast where particular words were spoken), simple data entry (e.g., entering a credit card number), preparation of structured documents (e.g. a radiology report), determining speaker characteristics, [5] speech-to-text processing (e.g., word processors emails , and generating a string-searchable transcript from an audio track), and aircraft (usually termed direct voice input ).

The term voice recognition [6] [7] [8] or speaker identification [9] [10] refers to identifying the speaker, rather than what they are saying. Recognizing the speaker can simplify the task of translating speech in systems that have been trained on a specific person's voice or it can be used to authenticate or verify the identity of a speaker as part of a security process.

From the technology perspective, speech recognition has a long history with several waves of major innovations. Most recently, the field has benefited from advances in deep learning and big data . The advances are evidenced not only by the surge of academic papers published in the field, but more importantly by the worldwide industry adoption of a variety of deep learning methods in designing and deploying speech recognition systems.

Models, methods, and algorithms

Both acoustic modeling and language modeling are important parts of modern statistically-based speech recognition algorithms. Hidden Markov models (HMMs) are widely used in many systems. Language modeling is also used in many other natural language processing applications such as document classification or statistical machine translation .

• Hidden Markov Model
• Dynamic Time Warping
• Neural Networks
• End-to-End Automated Speech Recognition

Learning Task: Applications

The following learning tasks focus on different applications of Speech Recognition. Explore the different applications.

• In-Car Systems
• People with Disabilities
• Health Care
• Telephone Support Systems

Usage in education and daily life

For language learning , speech recognition can be useful for learning a second language . It can teach proper pronunciation, in addition to helping a person develop fluency with their speaking skills. [11]

Students who are blind (see Blindness and education ) or have very low vision can benefit from using the technology to convey words and then hear the computer recite them, as well as use a computer by commanding with their voice, instead of having to look at the screen and keyboard. [12]

Students who are physically disabled or suffer from Repetitive strain injury /other injuries to the upper extremities can be relieved from having to worry about handwriting, typing, or working with scribe on school assignments by using speech-to-text programs. They can also utilize speech recognition technology to freely enjoy searching the Internet or using a computer at home without having to physically operate a mouse and keyboard. [12]

Speech recognition can allow students with learning disabilities to become better writers. By saying the words aloud, they can increase the fluidity of their writing, and be alleviated of concerns regarding spelling, punctuation, and other mechanics of writing. [13] Also, see Learning disability .

Use of voice recognition software, in conjunction with a digital audio recorder and a personal computer running word-processing software has proven to be positive for restoring damaged short-term-memory capacity, in stroke and craniotomy individuals.

Further applications

• Aerospace (e.g. space exploration , spacecraft , etc.) NASA's Mars Polar Lander used speech recognition technology from Sensory, Inc. in the Mars Microphone on the Lander [14]
• Automatic subtitling with speech recognition
• Automatic emotion recognition [15]
• Automatic translation
• Court reporting (Real time Speech Writing)
• eDiscovery (Legal discovery)
• Hands-free computing : Speech recognition computer user interface
• Home automation
• Interactive voice response
• Mobile telephony , including mobile email
• Multimodal interaction
• Pronunciation evaluation in computer-aided language learning applications
• Real Time Captioning [ citation needed ]
• Speech to text (transcription of speech into text, real time video captioning , Court reporting )
• Telematics (e.g. vehicle Navigation Systems)
• Transcription (digital speech-to-text)
• Video games , with Tom Clancy's EndWar and Lifeline as working examples
• Virtual assistant (e.g. Apple's Siri )

Further information

Conferences and journals.

Popular speech recognition conferences held each year or two include SpeechTEK and SpeechTEK Europe, ICASSP , Interspeech /Eurospeech, and the IEEE ASRU. Conferences in the field of natural language processing , such as ACL , NAACL , EMNLP, and HLT, are beginning to include papers on speech processing . Important journals include the IEEE Transactions on Speech and Audio Processing (later renamed IEEE Transactions on Audio, Speech and Language Processing and since Sept 2014 renamed IEEE /ACM Transactions on Audio, Speech and Language Processing—after merging with an ACM publication), Computer Speech and Language, and Speech Communication.

Books like "Fundamentals of Speech Recognition" by Lawrence Rabiner can be useful to acquire basic knowledge but may not be fully up to date (1993). Another good source can be "Statistical Methods for Speech Recognition" by Frederick Jelinek and "Spoken Language Processing (2001)" by Xuedong Huang etc. More up to date are "Computer Speech", by Manfred R. Schroeder , second edition published in 2004, and "Speech Processing: A Dynamic and Optimization-Oriented Approach" published in 2003 by Li Deng and Doug O'Shaughnessey. The recently updated textbook Speech and Language Processing (2008) by Jurafsky and Martin presents the basics and the state of the art for ASR. Speaker recognition also uses the same features, most of the same front-end processing, and classification techniques as is done in speech recognition. A most recent comprehensive textbook, "Fundamentals of Speaker Recognition" is an in depth source for up to date details on the theory and practice. [16] A good insight into the techniques used in the best modern systems can be gained by paying attention to government sponsored evaluations such as those organised by DARPA (the largest speech recognition-related project ongoing as of 2007 is the GALE project, which involves both speech recognition and translation components).

A good and accessible introduction to speech recognition technology and its history is provided by the general audience book "The Voice in the Machine. Building Computers That Understand Speech" by Roberto Pieraccini (2012).

The most recent book on speech recognition is Automatic Speech Recognition: A Deep Learning Approach (Publisher: Springer) written by D. Yu and L. Deng and published near the end of 2014, with highly mathematically oriented technical detail on how deep learning methods are derived and implemented in modern speech recognition systems based on DNNs and related deep learning methods. [17] A related book, published earlier in 2014, "Deep Learning: Methods and Applications" by L. Deng and D. Yu provides a less technical but more methodology-focused overview of DNN-based speech recognition during 2009–2014, placed within the more general context of deep learning applications including not only speech recognition but also image recognition, natural language processing, information retrieval, multimodal processing, and multitask learning. [18]

In terms of freely available resources, Carnegie Mellon University 's Sphinx toolkit is one place to start to both learn about speech recognition and to start experimenting. Another resource (free but copyrighted) is the HTK book (and the accompanying HTK toolkit). For more recent and state-of-the-art techniques, Kaldi toolkit can be used [19] . In 2017 Mozilla launched the open source project called Common Voice [20] to gather big database of voices that would help build free speech recognition project DeepSpeech (available free at GitHub ) [21] using Google open source platform TensorFlow [22] .

A demonstration of an on-line speech recognizer is available on Cobalt's webpage. [23]

For more software resources, see List of speech recognition software .

• Applications of artificial intelligence
• Articulatory speech recognition
• Audio mining
• Audio-visual speech recognition
• Automatic Language Translator
• Automotive head unit
• Cache language model
• Digital audio processing
• Dragon NaturallySpeaking
• Fluency Voice Technology
• Google Voice Search
• IBM ViaVoice
• Keyword spotting
• Multimedia information retrieval
• Origin of speech
• Phonetic search technology
• Speaker diarisation
• Speaker recognition
• Speech analytics
• Speech interface guideline
• Speech recognition software for Linux
• Speech Synthesis
• Speech verification
• Subtitle (captioning)
• Windows Speech Recognition
• List of emerging technologies
• Outline of artificial intelligence
• Timeline of speech and voice recognition
• ↑ Pacheco-Tallaj, Natalia M., and Claudio-Palacios, Andrea P. "Development of a Vocabulary and Grammar for an Open-Source Speech-driven Programming Platform to Assist People with Limited Hand Mobility". Research report submitted to Keyla Soto, UHS Science Professor.
• ↑ Stodden, Robert A., and Kelly D. Roberts. "The Use Of Voice Recognition Software As A Compensatory Strategy For Postsecondary Education Students Receiving Services Under The Category Of Learning Disabled." Journal Of Vocational Rehabilitation 22.1 (2005): 49--64. Academic Search Complete. Web. 1 Mar. 2015.
• ↑ Zaman, S., & Slany, W. (2014). Smartphone-Based Online and Offline Speech Recognition System for ROS-Based Robots. Information Technology and Control, 43(4), 371-380.
• ↑ P. Nguyen (2010). "Automatic classification of speaker characteristics" .
• ↑ "British English definition of voice recognition" . Macmillan Publishers Limited. Archived from the original on 16 September 2011 . Retrieved 21 February 2012 .
• ↑ "voice recognition, definition of" . WebFinance, Inc. Archived from the original on 3 December 2011 . Retrieved 21 February 2012 .
• ↑ "The Mailbag LG #114" . Linuxgazette.net. Archived from the original on 19 February 2013 . Retrieved 15 June 2013 .
• ↑ Reynolds, Douglas; Rose, Richard (January 1995). "Robust text-independent speaker identification using Gaussian mixture speaker models" . IEEE Transactions on Speech and Audio Processing 3 (1): 72–83. doi: 10.1109/89.365379 . ISSN  1063-6676 . OCLC  26108901 . Archived from the original on 8 March 2014 . https://web.archive.org/web/20140308001101/http://www.cs.toronto.edu/~frank/csc401/readings/ReynoldsRose.pdf . Retrieved 21 February 2014 .  
• ↑ "Speaker Identification (WhisperID)" . Microsoft Research . Microsoft. Archived from the original on 25 February 2014 . Retrieved 21 February 2014 . When you speak to someone, they don't just recognize what you say: they recognize who you are. WhisperID will let computers do that, too, figuring out who you are by the way you sound.
• ↑ Cerf, Vinton; Wrubel, Rob; Sherwood, Susan. "Can speech-recognition software break down educational language barriers?" . Curiosity.com . Discovery Communications. Archived from the original on 7 April 2014 . Retrieved 26 March 2014 .
• ↑ 12.0 12.1 "Speech Recognition for Learning" . National Center for Technology Innovation. 2010. Archived from the original on 13 April 2014 . Retrieved 26 March 2014 .
• ↑ Follensbee, Bob; McCloskey-Dale, Susan (2000). "Speech recognition in schools: An update from the field" . Technology And Persons With Disabilities Conference 2000 . Archived from the original on 21 August 2006 . Retrieved 26 March 2014 .
• ↑ "Projects: Planetary Microphones" . The Planetary Society. Archived from the original on 27 January 2012.
• ↑ Caridakis, George; Castellano, Ginevra; Kessous, Loic; Raouzaiou, Amaryllis; Malatesta, Lori; Asteriadis, Stelios; Karpouzis, Kostas (19 September 2007). Multimodal emotion recognition from expressive faces, body gestures and speech (in en). 247 . Springer US. 375–388. doi: 10.1007/978-0-387-74161-1_41 . ISBN  978-0-387-74160-4 .  
• ↑ Beigi, Homayoon (2011). Fundamentals of Speaker Recognition . New York: Springer. ISBN  978-0-387-77591-3 . Archived from the original on 31 January 2018 . https://web.archive.org/web/20180131140911/http://www.fundamentalsofspeakerrecognition.org/ .  
• ↑ Yu, D.; Deng, L. (2014). Automatic Speech Recognition: A Deep Learning Approach (Publisher: Springer) .  
• ↑ Deng, Li; Yu, Dong (2014). "Deep Learning: Methods and Applications" . Foundations and Trends in Signal Processing 7 (3–4): 197–387. doi: 10.1561/2000000039 . Archived from the original on 22 October 2014 . https://web.archive.org/web/20141022161017/http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf .  
• ↑ Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., ... & Vesely, K. (2011). The Kaldi speech recognition toolkit. In IEEE 2011 workshop on automatic speech recognition and understanding (No. CONF). IEEE Signal Processing Society.
• ↑ https://voice.mozilla.org
• ↑ https://github.com/mozilla/DeepSpeech
• ↑ https://www.tensorflow.org/tutorials/sequences/audio_recognition
• ↑ https://demo-cubic.cobaltspeech.com/

Further reading

• Pieraccini, Roberto (2012). The Voice in the Machine. Building Computers That Understand Speech. . The MIT Press. ISBN  978-0262016858 .  
• Woelfel, Matthias; McDonough, John (2009-05-26). Distant Speech Recognition . Wiley. ISBN  978-0470517048 .  
• Karat, Clare-Marie; Vergo, John; Nahamoo, David (2007). "Conversational Interface Technologies". In Sears, Andrew ; Jacko, Julie A.. The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies, and Emerging Applications (Human Factors and Ergonomics) . Lawrence Erlbaum Associates Inc. ISBN  978-0-8058-5870-9 .  
• Cole, Ronald; Mariani, Joseph; Uszkoreit, Hans et al., eds (1997). Survey of the state of the art in human language technology . Cambridge Studies in Natural Language Processing. XII–XIII . Cambridge University Press. ISBN  978-0-521-59277-2 .  
• Junqua, J.-C.; Haton, J.-P. (1995). Robustness in Automatic Speech Recognition: Fundamentals and Applications . Kluwer Academic Publishers. ISBN  978-0-7923-9646-8 .  
• Pirani, Giancarlo, ed (2013). Advanced algorithms and architectures for speech understanding . Springer Science & Business Media. ISBN  978-3-642-84341-9 .  

External links

• Signer, Beat and Hoste, Lode: SpeeG2: A Speech- and Gesture-based Interface for Efficient Controller-free Text Entry , In Proceedings of ICMI 2013, 15th International Conference on Multimodal Interaction, Sydney, Australia, December 2013
• Speech Technology at the Open Directory Project

Page Information

This page was based on the following wikipedia-source page :

• Speech Recognition https://en.wikipedia.org/wiki/Speech%20Recognition
• Date: 7/2/2019 - Source History
• Wikipedia2Wikiversity-Converter : https://niebert.github.com/Wikipedia2Wikiversity

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speech recognition

[ speech rek- uh g- nish - uh n ]

• automatic speech recognition
• Also called speak·er rec·og·ni·tion . the computerized analysis of spoken input to identify a speaker, as for a security system.
• the understanding of continuous speech by a computer

Word History and Origins

Origin of speech recognition 1

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Famously, the Defense Advanced Research Projects Agency developed the technologies behind the internet and speech recognition.

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Imagine a system that automatically does speech recognition, that’s easy, and then does fact-checking.

It’s the combination of three different skills…speech recognition, natural language processing and voice generation.

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Voice recognition (speaker recognition).

• Alexander S. Gillis, Technical Writer and Editor

What is voice recognition (speaker recognition)?

Voice or speaker recognition is the ability of a machine or program to receive and interpret dictation or to understand and perform spoken commands. Voice recognition has gained prominence and use with the rise of artificial intelligence ( AI ) and intelligent assistants, such as Amazon's Alexa and Apple's Siri .

Voice recognition systems let consumers interact with technology simply by speaking to it, enabling hands-free requests, reminders and other simple tasks.

Voice recognition can identify and distinguish voices using automatic speech recognition (ASR) software programs. Some ASR programs require users first train the program to recognize their voice for a more accurate speech-to-text conversion. Voice recognition systems evaluate a voice's frequency, accent and flow of speech.

Although voice recognition and speech recognition are referred to interchangeably, they aren't the same, and a critical distinction must be made. Voice recognition identifies the speaker, whereas speech recognition evaluates what is said.

This article is part of

A guide to artificial intelligence in the enterprise

• Which also includes:
• 10 top AI and machine learning trends for 2024
• 10 top artificial intelligence certifications and courses for 2024
• The future of AI: What to expect in the next 5 years

How does voice recognition work?

Voice recognition software on computers requires analog audio to be converted into digital signals, known as analog-to-digital (A/D) conversion. For a computer to decipher a signal, it must have a digital database of words or syllables as well as a quick process for comparing this data to signals. The speech patterns are stored on the hard drive and loaded into memory when the program is run. A comparator checks these stored patterns against the output of the A/D converter -- an action called pattern recognition .

In practice, the size of a voice recognition program's effective vocabulary is directly related to the RAM capacity of the computer in which it's installed. A voice recognition program runs many times faster if the entire vocabulary can be loaded into RAM compared to searching the hard drive for some of the matches. Processing speed is critical, as it affects how fast the computer can search the RAM for matches.

Audio also must be processed for clarity, so some devices may filter out background noise. In some voice recognition systems, certain frequencies in the audio are emphasized so the device can recognize a voice better.

Voice recognition systems analyze speech through one of two models: the hidden Markov model and neural networks. The hidden Markov model breaks down spoken words into their phonemes, while recurrent neural networks use the output from previous steps to influence the input to the current step.

As uses for voice recognition technology grow and more users interact with it, the organizations implementing voice recognition software will have more data and information to feed into neural networks for voice recognition systems. This improves the capabilities and accuracy of voice recognition products.

The popularity of smartphones opened up the opportunity to add voice recognition technology into consumer pockets, while home devices -- such as Google Home and Amazon Echo -- brought voice recognition technology into living rooms and kitchens.

Voice recognition uses

The uses for voice recognition have grown quickly as AI, machine learning and consumer acceptance have matured. Examples of how voice recognition is used include the following:

• Virtual assistants. Siri, Alexa and Google virtual assistants all implement voice recognition software to interact with users. The way consumers use voice recognition technology varies depending on the product. But they can use it to transcribe voice to text, set up reminders, search the internet and respond to simple questions and requests, such as play music or share weather or traffic information.
• Smart devices. Users can control their smart homes – including smart thermostats and smart speakers -- using voice recognition software.
• Automated phone systems. Organizations use voice recognition with their phone systems to direct callers to a corresponding department by saying a specific number.
• Conferencing. Voice recognition is used in live captioning a speaker so others can follow what is said in real time as text.
• Bluetooth. Bluetooth systems in modern cars support voice recognition to help drivers keep their eyes on the road. Drivers can use voice recognition to perform commands such as "call my office."
• Dictation and voice recognition software. These tools can help users dictate and transcribe documents without having to enter text using a physical keyboard or mouse.
• Government. The National Security Agency has used voice recognition systems dating back to 2006 to identify terrorists and spies or to verify the audio of anyone speaking.

Voice recognition advantages and disadvantages

Voice recognition offers numerous benefits:

• Consumers can multitask by speaking directly to their voice assistant or other voice recognition technology.
• Users who have trouble with sight can still interact with their devices.
• Machine learning and sophisticated algorithms help voice recognition technology quickly turn spoken words into written text.
• This technology can capture speech faster than some users can type. This makes tasks like taking notes or setting reminders faster and more convenient.

However, some disadvantages of the technology include the following:

• Background noise can produce false input.
• While accuracy rates are improving, all voice recognition systems and programs make errors.
• There's a problem with words that sound alike but are spelled differently and have different meanings -- for example, hear and here . This issue might be largely overcome using stored contextual information. However, this requires more RAM and faster processors .

For more on artificial intelligence in the enterprise, read the following articles:

What is an intelligent agent?

What is artificial general intelligence?

What is artificial superintelligence?

What is language modeling?

What is natural language processing?

History of voice recognition

Voice recognition technology has grown exponentially over the past five decades. Dating back to 1976, computers could only understand slightly more than 1,000 words. That total jumped to roughly 20,000 in the 1980s as IBM continued to develop voice recognition technology.

In 1952, Bell Laboratories invented AUDREY -- the Automatic Digit Recognizer -- which could only understand the numbers zero through nine. In the early to mid-1970s, the U.S. Department of Defense started contributing toward speech recognition system development, funding the Defense Advanced Research Projects Agency Speech Understanding Research. Harpy, developed by Carnegie Mellon, was another voice recognition system at the time and could recognize up to 1,011 words.

The company Dragon in 1990 launched the first speaker recognition product for consumers, Dragon Dictate. This was later replaced by Dragon NaturallySpeaking from Nuance Communications. In 1997, IBM introduced IBM ViaVoice, the first voice recognition product that could recognize continuous speech.

Apple introduced Siri in 2011, and it's still a prominent voice recognition assistant. In 2016, Google launched its Google Assistant for phones. Voice recognition systems can be found in devices including phones, smart speakers, laptops, desktops and tablets as well as in software like Dragon Professional and Philips SpeechLive.

During this past decade, several other technology leaders have developed more sophisticated voice recognition software, such as Amazon Alexa, for example. Released in 2014, Amazon Alexa also acts as a personal assistant that responds to voice commands. Currently, voice recognition software is available for Windows, Mac, Android, iOS and Windows phone devices.

Continue Reading About voice recognition (speaker recognition)

What's the difference between speech and voice recognition.

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What does speech recognition mean?

Definitions for speech recognition speech recog·ni·tion, this dictionary definitions page includes all the possible meanings, example usage and translations of the word speech recognition ., wiktionary rate this definition: 0.0 / 0 votes.

speech recognition noun

Voice recognition.

Wikidata Rate this definition: 0.0 / 0 votes

Speech recognition

In computer science, speech recognition is the translation of spoken words into text. It is also known as "automatic speech recognition", "ASR", "computer speech recognition", "speech to text", or just "STT". Some SR systems use "speaker independent speech recognition" while others use "training" where an individual speaker reads sections of text into the SR system. These systems analyze the person's specific voice and use it to fine tune the recognition of that person's speech, resulting in more accurate transcription. Systems that do not use training are called "speaker independent" systems. Systems that use training are called "speaker dependent" systems. Speech recognition applications include voice user interfaces such as voice dialing, call routing, domotic appliance control, search, simple data entry, preparation of structured documents, speech-to-text processing, and aircraft. The term voice recognition refers to finding the identity of "who" is speaking, rather than what they are saying. Recognizing the speaker can simplify the task of translating speech in systems that have been trained on specific person's voices or it can be used to authenticate or verify the identity of a speaker as part of a security process.

How to pronounce speech recognition?

Alex US English David US English Mark US English Daniel British Libby British Mia British Karen Australian Hayley Australian Natasha Australian Veena Indian Priya Indian Neerja Indian Zira US English Oliver British Wendy British Fred US English Tessa South African

How to say speech recognition in sign language?

Chaldean Numerology

The numerical value of speech recognition in Chaldean Numerology is: 9

Pythagorean Numerology

The numerical value of speech recognition in Pythagorean Numerology is: 5

Examples of speech recognition in a Sentence

Mukesh Ambani :

We now have the capability to develop truly India-native solutions, including speech recognition and natural language understanding for all major Indian languages and dialects.

• ^  Wiktionary https://en.wiktionary.org/wiki/Speech_Recognition
• ^  Wikidata https://www.wikidata.org/w/index.php?search=speech recognition

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• Published: 09 August 2024

Responsible development of clinical speech AI: Bridging the gap between clinical research and technology

• Visar Berisha   ORCID: orcid.org/0000-0001-8804-8874 1 &
• Julie M. Liss 2  

npj Digital Medicine volume  7 , Article number:  208 ( 2024 ) Cite this article

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This perspective article explores the challenges and potential of using speech as a biomarker in clinical settings, particularly when constrained by the small clinical datasets typically available in such contexts. We contend that by integrating insights from speech science and clinical research, we can reduce sample complexity in clinical speech AI models with the potential to decrease timelines to translation. Most existing models are based on high-dimensional feature representations trained with limited sample sizes and often do not leverage insights from speech science and clinical research. This approach can lead to overfitting, where the models perform exceptionally well on training data but fail to generalize to new, unseen data. Additionally, without incorporating theoretical knowledge, these models may lack interpretability and robustness, making them challenging to troubleshoot or improve post-deployment. We propose a framework for organizing health conditions based on their impact on speech and promote the use of speech analytics in diverse clinical contexts beyond cross-sectional classification. For high-stakes clinical use cases, we advocate for a focus on explainable and individually-validated measures and stress the importance of rigorous validation frameworks and ethical considerations for responsible deployment. Bridging the gap between AI research and clinical speech research presents new opportunities for more efficient translation of speech-based AI tools and advancement of scientific discoveries in this interdisciplinary space, particularly if limited to small or retrospective datasets.

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Introduction.

Recently, there has been a surge in interest in leveraging the acoustic properties (how it sounds) and linguistic content (what is said) of human speech as biomarkers for various health conditions. The underlying premise is that disturbances in neurological, mental, or physical health, which affect the speech production mechanism, can be discerned through alterations in speech patterns. As a result, there is a growing emphasis on developing AI models that use speech for the diagnosis, prognosis, and monitoring of conditions such as mental health 1 , 2 , 3 , 4 , 5 , cognitive disorders 6 , 7 , 8 , 9 , 10 , and motor diseases 11 , 12 , 13 , 14 , 15 , among others.

The development of clinical speech AI has predominantly followed a supervised learning paradigm, building on the success of data-driven approaches for consumer speech applications 16 , 17 . For instance, analysis of published speech-based models for dementia reveals that most models rely on high-dimensional speech and language representations 18 , either explicitly extracted or obtained from acoustic foundation models 19 , 20 and language foundation models 21 , 22 , to predict diagnostic labels 9 , 23 , 24 , 25 ; a similar trend is observed for depression 5 , 26 . The foundational models, initially pre-trained on data from general populations, are subsequently fine-tuned using clinical data to improve predictive accuracy for specific conditions. While data-driven classification models based on deep learning have worked well for data-rich applications like automatic speech recognition (ASR), the challenges in high-stakes clinical speech technology are distinctly different due to a lack of data availability at scale. For example, in the ASR literature, speech corpora can amount to hundreds of thousands of hours of speech samples and corresponding transcripts upon which models can be robustly trained in supervised fashion 16 , 17 . In contrast, currently available clinical datasets are much smaller, with the largest samples in the meta-analysis 9 , 24 , 25 consisting of only tens to hundreds of minutes of speech or a few thousand words. This is because clinical data collection is inherently more challenging than in other speech-based applications. Clinical populations are more diverse and present with variable symptoms that must be simultaneously collected with the speech samples, ensuring proper sampling from relevant strata.

Compounding the data problem is the fact that the ground truth accuracy of diagnostic labels for different conditions where speech is impacted varies from 100% certainty to less than 50% certainty, particularly in the early stages of disease when mild symptoms are nonspecific and present similarly across many different diseases 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 . Retrospective data often used to train published models does not always report diagnostic label accuracy or the criteria used to arrive at a diagnosis. Collecting representative, longitudinal speech corpora with paired consensus diagnoses is time-intensive and further impedes the development of large-scale corpora, which are required for developing diagnostic models based on supervised learning. Unfortunately, supervised models built on smaller-scale corpora often exhibit over-optimistic performance in controlled environments 35 and fail to generalize in out-of-sample deployments 36 , 37 . This begs the question of how we can successfully harness the power of AI to advance clinical practice and population health in the context of data availability constraints.

Here we propose that the clinical data constraints provide an opportunity for co-design of new analytics pipelines with lower sample complexity in collaboration with the clinical speech science community. The clinical speech science community has long studied the correlational and causal links between various health conditions and speech characteristics 38 , 39 , 40 , 41 , 42 . This research has focused on the physiological, neurological, and psychological aspects of speech production and perception, primarily through acoustic analysis of the speech signal, and linguistic analysis of spoken language. They involve interpretable and conceptually meaningful attributes of speech, often measured perceptually 43 , via functional rating scales 15 , or self-reported questionnaires 44 . Contributions from speech scientists, neuroscientists, and clinical researchers have deepened our understanding of human speech production mechanisms and their neural underpinnings, and particularly how neurodegeneration manifests as characteristic patterns of speech decline across clinical conditions 43 , 45 .

A co-design of a new explainable analytics pipeline can intentionally integrate scientific insights from speech science and clinical research into existing supervised models. We hypothesize that this will reduce timelines to translation, therefore providing an opportunity to grow clinical data scale through in-clinic use. As data size grows, data-driven methods with greater analytic flexibility can be used to discover new relations between speech and different clinical conditions and to develop more nuanced analytical models that can be confidently deployed for high-stakes clinical applications.

Bridging the gap between speech AI and clinical speech research leads to new opportunities in both fields. There is a clear benefit to the development of more sensitive tools for the assessment of speech for the clinical speech community. Existing instruments for assessment of speech exhibit variable within-rater and between-rater variability 46 . Developing objective proxies for these clinically-relevant constructs has the potential for increased sensitivity and reduced variability. More sensitive objective measures can also catalyze scientific discovery, enabling the identification of yet-to-be-discovered speech patterns across different clinical conditions. Conversely, effectively connecting speech AI research with clinical research enables AI developers to prioritize challenges directly aligned with clinical needs and streamline model building by leveraging domain-specific knowledge to mitigate the need for large datasets. To date, model developers have often overlooked feasibility constraints imposed by the inherent complexity of the relationship between speech production and the condition of interest. For example, recent efforts in clinical speech AI have focused on the cross-sectional classification of depression from short speech samples 5 , 26 . Given the well-documented variability in speech production 47 , the limitations of existing instruments for detecting depression 40 , and the heterogeneity in the manifestation of depression symptoms 48 , it is unlikely that stand-alone speech-based models will yield high-accuracy diagnostic models. Other studies have proposed using speech to predict conditions like coronary artery disease 49 or diabetes 50 . However, to the best of our knowledge, there is no substantial literature supporting the hypothesis that speech changes are specific enough to these conditions to serve as stand-alone indicators. In working with small data sets, understanding the approximate limits of prediction is critical for resource allocation and avoiding unwarranted conclusions that could lead to premature model deployment.

This perspective article advocates for a stronger link between the speech AI community and clinical speech community for the development of scientifically-grounded explainable models in clinical speech analytics. We begin by presenting a new framework for organizing clinical conditions based on their impact on the speech production mechanism (see Fig. 1 ). We believe such a framework is important to facilitate a shared understanding of the impact of clinical conditions on speech and stimulate interdisciplinary thought and discussion. It is useful in categorizing health conditions by the complexity and uncertainty they present for speech-based clinical AI models and provides a mental model for considering the inherent limitations of speech-based classification across different conditions. It orients researchers to consider the challenges posed by limited clinical datasets during model development, and helps prevent frequent methodological errors. This has the potential to expedite progress and further foster collaboration between the speech AI community and the clinical speech community. We then explore various contexts of use for speech analytics beyond cross-sectional classification, highlighting their clinical value and the value they provide to the clinical speech research community (see Fig. 2 ). The discussion further examines how the selected context of use influences model development and validation, advocating for the use of lower-dimensional, individually-validated and explainable measures with potential to reduce sample size requirements (see Fig. 3 ). The paper concludes with a discussion on ethical, privacy, and security considerations, emphasizing the importance of rigorous validation frameworks and responsible deployment (see Fig. 4 ).

The clinically-relevant information in speech

The production of spoken language is a complex, multi-stage process that involves precise integration of language, memory, cognition, and sensorimotor functions. Here we use the term ‘speech production’ to refer broadly to the culmination of these spoken language processes. There are several extant speech production models, each developed to accomplish different goals (see, for example 51 , 52 , 53 , 54 , 55 ). Common to these models is that speech begins with a person conceptualizing an idea to be communicated, formulating the language that will convey that idea, specifying the sensorimotor patterns that will actualize the language, and then speaking 56 :

Conceptualization: the speaker forms an abstract idea that they want to verbalize (Abstract idea formulation) and the intention to share through speech (Intent to speak).

Formulation: the speaker selects the words that best convey their idea and sequences them in an order allowed by the language (Linguistic formulation). Then they plan the sequence of phonemes and the prosodic pattern of the speech to be produced (Morphological encoding). Next, they program a sequence of neuromuscular commands to move speech structures (Phonetic encoding).

Articulation: the speaker produces words via synergistic movement of the speech production system. Respiratory muscles produce a column of air that drives the vocal folds (Phonation) to produce sound. This sound is shaped by the Articulator movements to produce speech. Two feedback loops (Acoustic feedback and Proprioceptive feedback) refine the neuromuscular commands produced during the Phonetic encoding stage over time.

Figure 1 introduces a hierarchy, or ordering, of health conditions based on how direct their impact is on the speech production mechanism. This hierarchy, motivated by initial work on speech and stress 57 , roughly aligns with the three stages of speech production and has direct consequences for building robust clinical speech models based on supervised learning.

The production of spoken language is a complex, multi-stage process that involves precise integration of language, memory, cognition, and sensorimotor functions. The three stages are Conceptualization, Formulation, and Articulation. This figure introduces a hierarchy, or ordering, of health conditions based on how direct their impact is on the speech production mechanism.

This hierarchy compels researchers to ask and answer three critical questions prior to engaging in AI model development for a particular health condition. First, how directly and specifically does the health condition impact speech and/or language? In general, the further upstream the impact of a health condition on speech, the more indeterminate and nuanced the manifestations become, making it challenging to build supervised classification models on diagnostic labels. As we move from lower to higher-order health conditions, there are more mediating variables between the health condition and the observed speech changes, making the relationship between the two more variable and complex.

The second question the model compels researchers to ask and answer is what are the sensitivity and specificity of ground truth labels for the health condition? In general (but with notable exceptions), the objective accuracy of ground truth labels for the presence or absence of a health condition generally becomes less certain from lower to higher-order conditions, adding noise and uncertainty to any supervised classification models built upon the labels. High specificity of ground truth labels is critical for the development of models that distinguish between health conditions with overlapping speech and language symptoms. The answers to these two questions provide a critical context for predicting the utility of an eventual model prior to model building.

Finally, the hierarchy asks model developers to consider the relevant clinical speech symptoms to be considered in the model. In Table 1 , we provide a more complete definition of each level in the hierarchy, a list of example conditions associated with the hierarchy, and primary speech symptoms associated with the condition. The list is not exhaustive and does not consider second and third-order impacts on speech. For example, Huntington’s disease (HD) has a first-order impact on speech causing hyperkinetic dysarthria (e.g. see Table 1 ). But it also has a second- and third-order impact to the extent one experiences cognitive issues and personality changes with the disease. Nevertheless, the table serves as a starting point for developing theoretically-grounded models. Directly modeling the subset of primary speech symptoms known to be impacted by the condition of interest may help reduce sample size requirements and result in smaller models that are more likely to generalize.

Ordering of health conditions based on speech impact

Zeroth-order conditions have direct, tangible effects on the speech production mechanism (including the structures of respiration, phonation, articulation, and resonance) that manifest in the acoustic signal, impacting the Articulation stage in our model in Fig. 1 . This impact of the physical condition on the acoustic signal can be understood using physical models of the vocal tract and vocal folds 58 that allow for precise characterization of the relationship between the health condition and the acoustics. As an example, benign vocal fold masses increase the mass of the epithelial cover of the vocal folds, thereby altering the stiffness ratio between the epithelial cover and the muscular body. The impact on vocal fold vibration and the resulting acoustic signal are amenable to modeling. These types of conditions are physically verifiable upon laryngoscopy, providing consistent ground truth labeling of the condition; and the direct relationship between the condition, its impact on the physical apparatus, and the voice acoustics is direct and quantifiable (although, note that differential diagnosis of vocal fold mass subtype is more difficult, see refs. 59 , 60 ). Thus, zeroth-order health conditions directly impact the speech apparatus anatomy and often have verifiable ground-truth labels.

First-order conditions interfere with the transduction of neuromuscular commands into movement of the articulators (e.g. dysarthria secondary to motor disorder). As with zeroth-order conditions, first-order conditions also disturb the physical speech apparatus and the Articulation stage in our model, however the cause is indirect. Injury or damage to the cortical and subcortical neural circuits and nerves impacts sensorimotor control of the speech structures by causing weakness, improper muscle tone and/or mis-scaling and incoordination of speech movements 61 . The sensorimotor control of speech movements is mediated through five neural pathways and circuits, each associated with a set of cardinal and overlapping speech symptoms: Upper and lower motor neuron pathways; the direct and indirect basal ganglia circuits; and the cerebellar circuit . Damage to these areas causes distinct changes in speech:

The lower motor neurons (cranial and spinal nerves, originating in brainstem and spinal cord, respectively) directly innervate speech musculature. Damage to lower motor neurons results in flaccid paralysis and reduced or absent reflexes in the muscles innervated by the damaged nerves, and a flaccid dysarthria when cranial nerves are involved.

The upper motor neurons originate in the motor cortex and are responsible for initiating and inhibiting activation of the lower motor neurons. Damage to upper motor neurons supplying speech musculature results in spastic paralysis and hyperreflexia, and a spastic dysarthria.

The basal ganglia circuit is responsible for facilitating and scaling motor programs and for inhibiting involuntary movements. Damage to the direct basal ganglia circuit causes too little movement (hypokinesia, as in Parkinson’s disease), resulting in a hypokinetic dysarthria; while damage to the indirect basal ganglia circuit causes too much movement (hyperkinesia, as in Huntington’s disease), resulting in a hyperkinetic dysarthria.

The cerebellar circuit is responsible for fine-tuning movements during execution. Damage to the cerebellar circuits result in incoordination, resulting in an ataxic dysarthria.

Speech symptoms are characteristic when damage occurs to any of these (or multiple) neural pathways, although there is symptom overlap and symptoms evolve in presence and severity as the disease progresses 61 . The diagnostic accuracy and test-retest reliability (within and between raters) of dysarthria speech labels from the speech signal alone (i.e., without knowledge of the underlying health condition) is known to be modest, except for expert speech-language pathologists with large and varied neurology caseloads 62 . Diagnosis of the corresponding health conditions relies on a physician’s clinical assessment and consideration of other confirmatory information beyond speech. Diagnostic accuracy is impacted by the physician’s experience and expertise, whether the symptoms presenting in the condition are textbook or unusual, and whether genetic, imaging, or other laboratory tests provide supporting or confirmatory evidence is available. For example, unilateral vocal fold paralysis is a first-order health condition with direct impact on the speech apparatus (impaired vocal fold vibration) and high-ground truth accuracy and specificity (can be visualized by laryngoscopy). In contrast, Parkinson’s disease (PD) has a diffuse impact on the speech apparatus (affecting phonation, articulation, and prosody) which is hard to distinguish from healthy speech or other similar health conditions (e.g., progressive supranuclear palsy) in early disease. The reported ground-truth accuracy of the initial clinical diagnosis ranges from 58% to 80%, calling into question clinical labels in early stage PD 28 .

Second-order conditions move away from the speech production mechanism’s structure and function and into the cognitive (i.e., memory and language) and perceptual processing domains. These conditions impact the Formulation stage of speaking and manifest as problems finding and sequencing the words to convey one’s intended message and may include deficits in speech comprehension. Alzheimer’s disease (AD) is a second-order condition that deserves particular attention because of the burgeoning efforts in the literature to develop robust supervised classification models 63 . AD disrupts the Formulation stage of speaking with word-finding problems, and the tendency to use simpler and more general semantic and syntactic structures. Natural language processing (NLP) techniques have been used to characterize these patterns and acoustic analysis has identified speech slowing with greater pausing while speaking, presumably because of decreased efficiency of cognitive processing and early sensorimotor changes 9 , 24 , 25 .

While the clinical study of speech and language in AD has consistently found evidence of such pattern changes in individuals diagnosed with probable AD, progress toward developing generalizable speech-based supervised learning clinical models for mild cognitive impairment (MCI) and AD has been relatively slow despite optimistic performance results reported in the literature 35 , 63 . We posit that this can be explained by answers to the first two questions that model in Fig. 1 compels researchers to consider. First, there is a lack of specificity of early speech and language symptoms to MCI and AD, given that the output is mediated by several intermediate stages and the variability associated with speech production. Mild and nonspecific speech and language symptoms will always pose a challenge for the development of clinical early detection/diagnostic speech tools until sufficient training data can result in the identification of distinct signatures (if they exist). Furthermore, given the current difficulty in accurately diagnosing MCI and AD, models based on supervised learning may be unwittingly using mislabeled training data and testing samples in their models. At present, AD is a clinical diagnosis, often preceded by a period of another clinical diagnosis of MCI. MCI is extremely difficult to diagnose with certainty, owing to variability in symptoms and their presentation over time, the overlap of speech and language symptoms with other etiologies, and the diagnostic reliance on self-report 33 . With the current absence of a definitive ground truth label for MCI or early Alzheimer’s disease, and the lack of specificity in speech changes, supervised learning models trained on small, questionably labeled data likely will continue to struggle to generalize to new data.

Third-order conditions impact the Conceptualization stage of speech production and include mental health conditions affecting mood and thought. These conditions can manifest in significant deficits and differences in speech and language, and this has been well-characterized in the literature 4 . For example, acoustic analysis can reveal rapid, pressed speech associated with mania, as well as slowed speech without prosodic variation that might accompany depression. Natural language processing can reveal and quantify disjointed and incoherent thought in the context of psychiatric disorders 64 . Despite this, the impact of these mood and thought conditions on the speech apparatus and language centers in the brain may be indirect and nonspecific relative to low-order conditions. Mental health conditions frequently cause a mixture or fluctuation of positive symptoms (e.g., hallucinations, mania) and negative symptoms (e.g., despondence, depression), which can present chronically, acutely, or intermittently. The associated speech and language patterns can be attributed to any number of other reasons (fatigue, anxiety, etc.) With regard to ground-truth accuracy and specificity, studies have shown that around half of schizophrenia diagnoses are inaccurate 65 . This problem has resulted in a push to identify objective biomarkers to distinguish schizophrenia from anxiety and other mood disorders 66 , 67 . This complicates the development of models for health condition detection and diagnosis; however, machine-learning models may be developed to objectively measure speech and language symptoms associated with specific symptomatology. For example, distinguishing between negative versus positive disease symptoms may be achievable with careful construction of speech elicitation tasks and normative reference data, given the central role that language plays in the definition of these symptoms 68 , 69 .

Across all health conditions, extraneous and comorbid factors can exert meaningful influence on speech production. For example, anxiety, depression, and fatigue, perhaps even as a consequence of an underlying illness, are known to impact the speech signal. It would not be straightforward to distinguish their influence from those of primary interest, adding complexity and uncertainty for models based on supervised learning, regardless of the health condition’s order. However, the increased variability in both data and diagnostic accuracy for many higher-order conditions makes speech-based models trained using supervised learning on small datasets vulnerable to reduced sensitivity and specificity. This is not merely a matter of augmenting the dimensionality of speech features or enlarging the dataset; it reflects the intrinsic variability in how humans generate speech. Finally, the accuracy and specificity of ground truth labels for health conditions are critical to consider in assessing the feasibility of interpretable model development. Unlike the static link between speech and the health condition, as diagnostic technologies advance and criteria evolve, the accuracy of these labels is expected to improve over time, thereby potentially enabling more robust model development.

Defining an appropriate context of use

As mentioned before, most published clinical speech AI development studies are based on supervised learning where developers build AI models to distinguish between two classes or to predict disease severity. This approach generally presumes the same context of use for clinical speech analytics across different applications: namely, the cross-sectional detection of a specific condition or a prediction of clinical severity based on a speech sample. As we established in the foregoing discussion, this approach, when combined with limited training data, is less likely to generalize.

Nevertheless, there are a number of use cases, in which speech analytics and AI can provide more immediate value and expedite model translation. These are outlined in Fig. 2 , where we explore these applications in greater depth. Focusing on these use cases will reduce timelines to translation, providing an opportunity to grow clinical data scale through in-clinic collection. With increased data size and diversity, researchers will better characterize currently-unknown fundamental limits of prediction for speech-based classification models for higher-order conditions (e.g. how well can we classify between depressed and non-depressed speech); and can bring to bear more advanced data-driven methods to problems that provide clinical value.

A listing of different contexts of use for the development and validation of clinical tools based on speech AI.

Diagnostic assistance

Despite rapid advancements in biomedical diagnostics, the majority of neurodegenerative diseases are diagnosed by the presence of cardinal symptoms on clinical exams. As discussed previously and as shown in Table 1 , many health conditions include changes in speech as a core symptom. For example, diagnosis of psychiatric conditions involves analysis of speech and language attributes, such as coherence, fluency, and tangentiality 70 . Likewise, many neurodegenerative diseases lead to dysarthria, and a confirmatory speech deficit pattern can be used to support their diagnoses 61 . Tools for the assessment of these speech deficit patterns in the clinical setting typically depend on the clinical judgment or on scales reported by patients themselves. There is a large body of evidence indicating that these methods exhibit variable reliability, both between different raters and within the same rater over time 46 , 62 . Clinical speech analytics has the potential to enhance diagnostic accuracy by providing objective measures of clinical speech characteristics that contribute to diagnosis, such as hypernasality, impaired vocal quality, and articulation issues in dysarthria; or measures of coherence and tangentiality in psychosis. These objective measures can provide utility for manual diagnosis in clinic or can be used as input into multi-modal diagnostic systems based on machine learning.

Non-specific risk assessment tools

While differential diagnosis based on speech alone is likely not possible for many conditions, progressive and unremitting changes in certain aspects of speech within an individual can be a sign of an underlying illness or disorder 61 . Clinical speech analytics can be used to develop tools that track changes in speech along specific dimensions known to be vulnerable to degradation in different conditions. This could provide value as an early-warning indicator, particularly as the US health system moves toward home-based care and remote patient monitoring. Such a tool could be used as a non-specific risk assessment tool triggering additional tests when key speech changes reach some threshold or is supported by changes in other monitored modalities.

Longitudinal tracking post-diagnosis

In many conditions, important symptoms can be tracked via speech post-diagnosis. For example, tracking bulbar symptom severity in ALS, as a proxy for general disease progression, can provide insights on when AAC devices should be considered or to inform end-of-life planning 71 . In Parkinson’s disease, longitudinal tracking of speech symptoms would be beneficial for drug titration 72 , 73 . In dementia, longitudinal tracking of symptoms measurable via speech (e.g. memory, cognitive-linguistic function) can provide valuable information regarding appropriate care and when changes need to be made.

Speech as a clinically meaningful endpoint

Speech is our principal means of communication and social interaction. Conditions that impair speech can severely hinder a patient’s communicative abilities, thereby diminishing their overall quality of life. Current methods for assessing communication outcomes include perceptual evaluations, such as listening and rating, or self-reported questionnaires 61 , 69 . In contrast to the use case as a solitary diagnostic tool, employing clinical speech analytics to objectively assess communicative abilities is inherently viable across many conditions. This is due to the direct correlation between the construct (communicative ability) and the input (speech). For instance, in dysarthria, clinical speech analytics may be utilized to estimate intelligibility, the percentage of words understood by listeners, which significantly affects communicative participation 74 . In psychosis, speech analytics can facilitate the creation of objective tools for assessing social competencies; these competencies are closely tied to quality of life indicators 69 . Similarly, in dementia, a decline in social interaction can lead to isolation and depression, perhaps hastening cognitive decline 75 . A related emerging use case in Alzheimer’s disease is providing context for blood-based diagnostics. As new biomarkers with confirmatory evidence of pathophysiology emerge, there will likely be an increase in Alzheimer’s diagnoses without co-occurring clinical-behavioral features. The group of patients with AD diagnoses, but without symptoms, will require context around this diagnosis. Speech analytics will be important as measures of behavioral change that are related to quality of life.

Improving clinical trial design

The Food and Drug Administration (FDA) prioritizes patient-relevant measures as endpoints in clinical trials. They have also identified speech and communication metrics as particularly underdeveloped for orphan diseases 76 . Objective and clinically-meaningful measures based on speech analytics that are collected more frequently can result in an improved sensitivity for detecting intervention effects. Such measures have the potential to decrease the required sample sizes for drug trials, enable more efficient enrollment, or to ascertain efficacy with greater efficiency 77 .

Facilitating development of digital therapeutics

There has been significant recent interest in development of digital therapeutics for various neurological and mental health conditions. Several of these devices target improving the patients’ social skills or communication abilities 78 . In this evolving space, introducing concrete digital markers of social competence allows for more efficient evaluation of efficacy and precision approaches for customizing therapeutics for the patient.

Development and validation of robust models

The context of use profoundly influences the development of clinical speech AI models, shaping their design, validation, and implementation strategies. For example, for contexts of use involving home monitoring, robustness to background noise, variability in recording conditions and usability are essential. For longitudinal monitoring, developed tools must be sensitive to subtle changes in speech characteristics relevant to the progression of the condition being monitored. This necessitates longitudinal data collection for development and validation to ensure stability and sensitivity over time. Screening tools in diverse populations require a training dataset that captures demographic variability to avoid bias. Solutions based on noisy diagnostic labels may require uncertainty modeling through Bayesian machine learning or ensemble methods that quantify prediction confidence 79 . Concurrently, techniques like label smoothing 80 and robust loss functions 81 can enhance model resilience under label noise.

Each context of use presents a custom development path to address the unique challenges and a parallel validation strategy that spans hardware, analytical validation, and clinical validation - see Fig. 3 . The current approach focused on data-driven supervised learning on diagnostic labels limits the development and understanding of new models and makes model validation challenging. While there are many validation metrics for evaluating AI model performance, the prevalent metrics in published speech-based models primarily focus on estimating “model accuracy” (e.g. what percent of the time does the model correctly classify between Healthy and Dementia labels based on speech) using a number of methods (e.g. cross-validation, held-out test accuracy). However, accurately estimating the model accuracy of high-dimensional supervised learning models is challenging, and current methods are prone to overoptimism 35 . In addition, many supervised machine learning models are sensitive to input perturbations, which is a significant concern for speech features known for their day-to-day variability 82 . Consequently, model performance diminishes with any temporal variation in the data.

The development of clinical speech AI models begins with a context of use. The context of use informs downstream development and validation of resulting models. The Verification, Analytical Validation, and Clinical Validation (V3) framework has been proposed as a conceptual framework for the initial validation of biometric monitoring technologies.

A starting point for clinical model validation is the Verification/Analytical Validation/Clinical Validation (V3) framework, a framework for validating digital biometric monitoring technologies. The original version of the framework proposes a structured approach with three evaluation levels: Verification of hardware, Analytical Validation, and Clinical Validation 83 . This framework has roots in principles of Verification and Validation for software quality product management and deployment 84 . While these existing validation systems are designed to confirm that the end system accurately measures what it purports to measure, the V3 framework adds the additional step of confirming that the clinical tools are meaningful to a defined clinical population. To that end, Verification ascertains the sensor data’s fidelity within its intended environment. Analytical validation examines the accuracy of algorithms processing sensor data to yield behavioral or physiological metrics, and clinical validation evaluates clinical model outputs with clinic ground truths or established measures known to be meaningful to patients. This includes existing clinical scales like the PHQ-9 (depression) or the UPDRS (Parkinson’s disease). In Fig. 3 we provide a high-level overview of the end-to-end development and validation process for clinical speech AI. It is important to note that the V3 is a conceptual framework that must be specifically instantiated for the validation of different clinical speech applications. While it can help guide the development of a validation plan, it does not provide one out of the box. Furthermore, this level of validation is only a starting point as the FDA suggests constant model monitoring post-deployment to ensure continued generalization 85 .

Supervised learning approaches based on uninterpretable input features and clinical diagnostic labels make adoption of the complete V3 framework challenging. Analytical validation is especially challenging as it’s difficult to ensure that learned speech representations are measuring or detecting physiological behaviors of interest. For example, in Parkinson’s disease, both the speaking rate and the rate of opening and closing of vocal folds is impacted. Uninterpretable features have unknown relationships with these behavioral and physiological parameters. As an alternative, model developers can use representations that are analytically validated relative to these constructs. This would lead to more interpretable clinical models. Validation should be approached end-to-end during the development process, with different stages (and purposes of analysis) employing different validation methods. Small-scale pilot tests may focus on parts of this framework. However, for work with deployment as a goal, ensuring generalizability and clinical utility requires validating the hardware on which the speech was collected, ensuring that intermediate representations are valid indicators of behavioral and physiological measures (e.g speaking rate, articulatory precision, language coherence), and clinical models developed using these speech measures are associated with existing clinical ground truths or scales that are meaningful to patients 86 .

Interpretable, clinically-important measures based on speech are currently missing from the literature. Clinically-relevant feature discovery and model performance evaluation in speech analytics are challenged by the high-dimensionality of speech, complex patterns, and limited datasets. Table 1 highlights several speech constructs that have been studied relative to various conditions; however, most of these constructs do not have standardized operational definitions in the clinical speech analytics literature. Instead, model developers rely on high-dimensional representations that have been developed for other purposes. For example, adopted from the ASR literature, many clinical models use representations based on mel-frequency cepstral coefficients or mel-spectra 18 ; or representations learned by pre-trained foundation models 19 , 20 . However, these features are not interpretable, making analytical and clinical validation challenging.

Development of a clinically-tailored speech representation could significantly refine the development process, favoring smaller, individually validated, and clinically-grounded features that allow scientists to make contact with the existing literature and mitigate model overfitting and variability. This field would benefit from a concerted and synergistic effort in the speech AI community and the speech science community to operationalize and validate a measurement model for the intermediate constructs like those listed in Table 1 87 . For example, in our previous work, we made progress in this direction by developing measurement models for the assessment of hypernasality and consontant-vowel transitions and used it to evaluate cleft lip and palate and dysarthria 88 , 89 ; several measures of volition and coherence for schizophrenia 69 ; and measures of semantic relevance for dementia 10 . Individually-validated interpretable measures allow for easier alignment to different contexts of use, integration within larger multi-modal systems, and establish a more direct link to the existing clinical literature. Furthermore, they can be used as a way of explaining the operation of larger, more complex models via bottleneck constraints 90 or they can be combined with new methods in causal machine learning for development of explainable models 91 .

Finally, clinically-interpretable representations can also play a pivotal role in integrating the patient’s perspective into the design of algorithms. The idea is that by aligning closely with the lived experiences and symptoms important to patients, these representations ensure that algorithmic outcomes resonate with the quality of life impact of health conditions. The hypothesis is that this patient-centric approach could have the added benefit of reinforcing patient trust and engagement in digital health.

Ethical, privacy, and security considerations

The deployment and regulation of clinical speech models in healthcare present multiple challenges and risks. Prematurely launched models (without robust validation) risk delivering clinically inaccurate results and potentially causing patient harm, while biases in model training can lead to skewed performance across diverse populations. Moreover, the use of speech data for health analytics raises significant privacy and security concerns. We outline these considerations in Fig. 4 and expand on them below.

An overview of key risks and corresponding mitigation strategies for the development of clinical speech AI models.

Premature deployment of inaccurate models

A primary risk of prematurely-deployed models is that they will provide clinically inaccurate output. As discussed in previous work 35 , current strategies to validate AI models are insufficient and produce overoptimistic estimates of accuracy. Several studies have highlighted this as a more general problem in AI-based science 92 , 93 . However, reported accuracy metrics carry much weight when presented to the public and can lead to premature deployment. There is considerable risk that these models will fail if deployed and potentially harm patients 94 . For example, consider the Cigna StressWaves Test model, deployed after only internal evaluation and no public efficacy data. This model analyzes a user’s voices to predict their stress level and is publicly available on the Cigna Website. Independent testing of the model reveals that it has poor test-retest reliability (measured via intraclass correlation) and poor agreement with existing instruments for measuring stress 37 .

Biased models

An additional risk of clinical speech-based models stems from the homogeneity of the data often used to train these models. Biological and socio-cultural differences contribute significantly to the variation in both the speech signal and the clinical conditions (impacting aspects from risk factors to treatment efficacy). Careful consideration of these differences in model building necessitates robust experiment design and representative stratification of data. However, a recent study demonstrates that published clinical AI models are heavily biased demographically, with 71% of the training data coming from only three states: California, Massachusetts, and New York, with 34 of the states not represented at all 95 . Similarly, analysis of clinical speech datasets indicates a significant skew towards the English language, overlooking the linguistic diversity of global populations. To accurately capture health-related speech variations, it’s essential to broaden data collection efforts to include a more representative range of the world’s native languages as health-related changes in speech can be native language-specific 96 . It becomes challenging to determine how models trained on unrepresentative data would perform when deployed for demographic groups for which they were not trained.

Privacy and security considerations

Speech and language data is widely available and, as we continue to interact with our mobile devices, we generate an ever-growing personal footprint of our health status. Previous studies have shown that this data (speeches, social media posts, interviews) can be analyzed for health analytics 97 , 98 , 99 . There is a risk that similar data on an even larger scale and over longer periods of time can be accessed by technology companies to make claims about the health or emotional state of their users without their permission or by national or international adversaries to advance a potentially false narrative on the health of key figures. The risks to the privacy of this type of analysis, if used outside of academic research, is considerable, with national and international political ramifications. Internally, political adversaries can advance a potentially false narrative on the health of candidates. Internationally, geopolitical adversaries could explore this as an additional dimension of influence in elections.

There is no silver bullet to reduce these risks, however, there are several steps that can be taken as mitigation strategies. With the public availability of speech technology, building AI models has become commoditized; however, the bottleneck remains prospective validation. Thorough validation of the model based on well-accepted frames such as the V3 framework is crucial prior to deployment 83 . This validation must extend beyond initial data sets and include diverse demographic groups to mitigate biases. Moreover, developers should engage in continuous post-deployment monitoring to identify and rectify any deviations in model performance or emergent biases. Transparency in methodology and results, coupled with responsible communication to the public, can reduce the risks of misperceived model accuracy.

On the privacy front, there are emerging technical solutions to parts of this problem based on differential privacy and federated learning 100 , 101 , 102 ; however, a complete socio-technical solution will require stringent data protection regulations and ethical guidelines to safeguard personal health information. First, it is wise to reconsider IRB review protocols in light of new technologies and publicly available data; in industry, proactive collaboration with regulatory bodies (e.g. FDA) can help establish clear guidelines. This is clear for companies focused on clinical solutions, however, the regulation of AI-based devices for technology companies, particularly those focused on wellness, is less well-defined. Recent guidance from the Federal Trade Commission (FTC) advising companies to only make evidence-backed claims about AI-driven products is a step in the right direction 103 .

Data availability

There is no data associated with this manuscript as it is a perspectives article centered around a theoretical framework.

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Berisha, V., Liss, J.M. Responsible development of clinical speech AI: Bridging the gap between clinical research and technology. npj Digit. Med. 7 , 208 (2024). https://doi.org/10.1038/s41746-024-01199-1

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EDITORIAL article

Editorial: the ethics of speech ownership in the context of neural control of augmented assistive communication.

• 1 Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
• 2 Applied Emotion and Motivation Psychology, Institute of Psychology and Education, Ulm University, Ulm, Germany

Editorial on the Research Topic The ethics of speech ownership in the context of neural control of augmented assistive communication

1 Introduction

This Research Topic focuses on the complex and unique ethical considerations and design challenges with respect to preserving user agency (the reflection of user intention in system performance) when augmented assistive communication (AAC) devices are controlled via neural signals. Such devices represent a special category of Brain-Computer Interfaces (BCIs) and AACs because they involved both direct sensing and interpretation of brain activity and assistive communication. The ethical discussions around BCIs and AACs fully apply Speech-BCIs (BCIs that produce speech content). However, when the challenges of interpreting neuronal signals intersect with AAC device imposed limitations on user expression unique issues arise. In addition, the recent growth in the technical capabilities of BCIs is driving a rapid expansion in possible use-cases for Speech-BCIs. Hence, this Research Topic aims to further and sharpen the discussion of ensuring user agency and accessing speech ownership in the context of Speech-BCIs at a time when key design decisions that can greatly influence these issues are still being made.

2 Main themes and topics

This discussion is explored in three original manuscripts by leading ethicists and researchers at the cutting edge of the Speech-BCI field, complemented by two reprinted works that help frame the context. Three central themes emerge:

1) Speech-BCIs represent shared control systems where users control cognitive activities and complex AI systems decoded brain signal features and infer intended speech output.

2) A clear definition of the unique ethical concerns and terms of the Speech-BCI field is needed to make the design challenges concrete. A clear definition of the unique ethical concerns and terms of the Speech-BCI field

3) The design choices to promote agency often weigh performance and speed against transparency of speech ownership .

A strong argument can be made that the improvements in speed and correctness facilitated by AIs such as Large Language Models (LLMs) offer increased user agency by expanding the user's ability to communicate. However, AI assistance adds an interpretive layer with potential biases on top of the often imperfect speech content directly decoded from neural signals. All three original works touch on this as a central tension point at the heart of the ethical discussions in the field.

3 Summary of original manuscripts

First, a narrative review of ethical issues prominent in Speech-BCI focused literature intended to build a framework for discussion of ethical design recommendations is given in van Stuijvenberg et al. . The concepts of designing for executory control [if and when speech is externalized ( Maslen and Rainey, 2021 )] vs. guidance control [the shaping of how speech is formulated ( Sankaran et al. )] that are emerging as guiding design principals in Speech-BCI literature are introduced. They also provide suggestions for clarifying the terminology used when discussing the ethics of Speech-BCI design decisions. They emphasize the importance of defining whether a Speech-BCI is used as an instrumental tool , for communicating simple messages, for example, about care needs and/or as an expressive tool , for communicating more complex opinions and emotions. When used as an instrumental tool, accuracy and transparency of meaning may be prioritized over speed and naturalness. Whereas, when used as an expressive tool, speed and fluency of language may be more relevant performance goals. While natural language is clearly used for both, Speech-BCIs are imperfect communication tools and clarifying their intended use sharpens ethical discussions. In addition , there is a clear difference in the context of Speech-BCIs between speech that is formalized internally, but not intended for external communication and speech meant to be externalized by the BCI user . However, the terminology used to describe this distinction has not been standardized in the field, potentially leading to misunderstandings, especially when communicating to the broader public.

The second original manuscript by Rainey broadens the discussion by pointing out that effective Speech-BCI use will be a learned skill . As such, users will need to learn to work with or around the system's limitations. Rainey discusses ways in which this skilled use can lead to a disconnect between the face value meaning of the produced speech and the users intended meaning even when executory and guidance control have been established. Rainey explores the ethical consequences a “reasons-responsiveness” [the “relationship between human reasons and the behavior of systems which include human and nonhuman agents” ( Mecacci and Santoni de Sio, 2020 )] ambiguity can have for the attribution of ownership of BCI-produced speech. This expands upon the issue of speech ownership when control over speech output is imperfect and/or shared as presented above.

Finally, a perspective of researchers at the forefront of Speech-BCIs use by people with severe speech disabilities is given by Sankaran et al. . They discuss practical strategies for executive control by allowing the user to review speech output before it is shared , also suggested by van Stuijvenberg et al. , and guidance control such as allowing users to choose language models with specific biases such as formal vs. informal speech.

4 Broader context

Additionally, two reprints provide a larger context for the discussion. A comparative review by Ishida et al. of the neuroethical issues represented in neuroethical and neuroscience journals stresses that both fields could benefit from integrating ethicists into research groups. Original research by Muncke et al. into the neural correlates of speech recognition in noisy environments highlights how context can effect speech-BCIs. A detailed discussion of the role human factors such as user mental states and traits like language competence and cultural background can have on BCI performance is given in a complementary Research Topic: Analyzing and computing humans – the role of language, culture, brain and health .

5 Conclusion and outlook

This Research Topic provides a basis to expand the discussion of the unique ethical issues inherent to direct neural control of AACs at a time when the exposure of Speech-BCIs in the media is rapidly increasing. As the public watches these devices transition from exciting concept to clinical reality it is critical to have clear discussion of the ethical implications of design decisions making this transition possible to avoid misconceptions about the aims and limitations of Speech-BCIs with respect to speech ownership that could jeopardize their acceptance.

Author contributions

ZF: Conceptualization, Writing – original draft, Writing – review & editing. JB: Writing – review & editing. CH: Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Maslen, H., and Rainey, S. (2021). Control and ownership of neuroprosthetic speech. Philos. Technol . 34, 425–445. doi: 10.1007/s13347-019-00389-0

PubMed Abstract | Crossref Full Text | Google Scholar

Mecacci, G., and Santoni de Sio, F. (2020). Meaningful human control as reason-responsiveness: the case of dual-mode vehicles. Ethics Inform. Technol . 22, 103–115. doi: 10.1007/s10676-019-09519-w

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Keywords: Brain-Computer Interfaces (BCI), augmented assistive communication (AAC), speech ownership, user agency, executory control, guidance control

Citation: Freudenburg Z, Berezutskaya J and Herbert C (2024) Editorial: The ethics of speech ownership in the context of neural control of augmented assistive communication. Front. Hum. Neurosci. 18:1468938. doi: 10.3389/fnhum.2024.1468938

Received: 22 July 2024; Accepted: 26 July 2024; Published: 06 August 2024.

Edited and reviewed by: Gernot R. Müller-Putz , Graz University of Technology, Austria

Copyright © 2024 Freudenburg, Berezutskaya and Herbert. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Zachary Freudenburg, z.v.freudenburg@umcutrecht.nl

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.