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A speech recognition system takes natural speech audio and produces a coded representation of what was said, such as a word, command, or sentence. It may be implemented in hardware, software, or both; the specific input devices and processing methods depend on the system.
What is a speech recognition system?
The International Telecommunication Union defines an automatic speech recognition (ASR) system as “an implementation in hardware or software that accepts a natural speech signal as an input and provides, at the output, a coded version of what has been said (word, command, expression, sentence, etc.).” That definition appears in ITU-T P.10/G.100 Amendment 2, published in May 2024: ITU-T P.10/G.100 Amendment 2.
In everyday use, “speech recognition,” “automatic speech recognition,” and “speech-to-text” often describe the task of identifying spoken words and returning them as text. The output need not always be ordinary text: a system may return a recognized command or another coded representation.
The U.S. Section 508 glossary similarly describes ASR as the use of computer hardware- and software-based procedures to identify and process words spoken into a system: Section 508 glossary.
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What does speech recognition do—and what does it not do?
Recognition identifies what was spoken. By itself, it does not necessarily determine what the speaker intends. An application can add a separate natural-language understanding component to interpret context and infer an action. Amazon’s Alexa Skills Kit explains the distinction: ASR converts spoken words into text, while language understanding adds interpretation: Alexa Skills Kit: Understanding the Voice Interaction Model.
For example, recognizing the words “turn on the lights” produces the words or a corresponding command representation. Deciding which lights the speaker means, and then controlling them, may require additional application logic or language understanding.
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How does a speech recognition system work?
At a high level, the system receives a speech signal, processes it with a recognition engine, and produces a textual or otherwise coded result. An implementation may combine an audio source such as a microphone, hardware, software runtime or API, recognition models, and vocabulary or grammar constraints. Not every system uses the same components or deployment arrangement.
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How can speech recognition be deployed?
Speech recognition can run on a device or use an online service, and it can process an existing recording or handle incoming audio as it arrives. These are implementation choices, not properties that apply identically to every system.
- Recorded audio or live audio: Microsoft documents a Windows API that supports batch transcription of a recorded file and streaming transcription from a microphone or another real-time source. See Microsoft’s Windows speech-recognition API documentation.
- On-device or remote processing: Microsoft separately documents Windows app recognition flows in which predefined dictation and web-search grammars use a remote service and need a network connection. That guidance describes those Windows flows, not a universal requirement for speech recognition. See Microsoft’s Windows speech-recognition guidance.
- Open dictation or constrained phrases: An application may accept broader dictation or narrow recognition to a vocabulary suited to a task. The Microsoft guidance for predefined grammars describes recognition of up to 10 seconds of speech input in that specific flow; the figure should not be generalized to other products or systems. Custom constraints can instead specify expected phrases.
A microphone is one possible way to supply speech audio, not a separate purchase every user must make. A computer may already have a built-in microphone or another audio input. Requirements depend on the system and how it is being used.
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Where is speech recognition used?
Common uses include dictation, transcription, voice commands, web searches, meeting or media captions, accessibility-related speech input, and hands-free interactions. A system designed for a small set of commands may use different vocabulary and evaluation criteria from one intended for open-ended dictation.
What should you consider when choosing or evaluating a system?
The right criteria depend on the task. For a meaningful comparison, check the conditions under which the system will receive speech and how its output will be used.
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- Audio input: Which microphones or other audio sources are supported, and under what conditions?
- Language and vocabulary: Does it support the language, accents, terminology, and range of words the task requires?
- Recognition scope: Is it intended for a fixed set of commands or unrestricted dictation?
- Processing mode: Does it handle recorded files, live streams, or both?
- Deployment and connectivity: Does processing take place on the device or remotely, and does the chosen flow require a network connection?
- Evaluation method: Are the tests appropriate to the intended use? ISO/IEC 30122-2:2017 covers criteria and test methods for voice commands and their recognition engine; ISO reports that the standard was confirmed in 2024 and remains current: ISO/IEC 30122-2:2017.
These dimensions help define a fair evaluation, but they do not establish a universal accuracy percentage. Performance claims need to identify the language, dataset, test conditions, and system version or configuration being measured.
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