speech recognition

speech recognition
Computers. the computerized analysis of spoken words in order to identify the speaker, as in security systems, or to respond to voiced commands: the analysis is performed by finding patterns in the spectrum of the incoming sound and comparing them with stored patterns of elements of sound, as phones, or of complete words. Also called voice recognition. Cf. phone2.

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Ability of computer systems to accept speech input and act on it or transcribe it into written language.

Current research efforts are directed toward applications of automatic speech recognition (ASR), where the goal is to transform the content of speech into knowledge that forms the basis for linguistic or cognitive tasks, such as translation into another language. Practical applications include database-query systems, information retrieval systems, and speaker identification and verification systems, as in telebanking. Speech recognition has promising applications in robotics, particularly development of robots that can "hear." See also pattern recognition.

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      the ability of machines to respond to spoken commands. Speech recognition enables “hands-free” control of various electronic devices—a particular boon to many disabled persons—and the automatic creation of “print-ready” dictation. Among the earliest applications for speech recognition were automated telephone systems and medical dictation software. It is still frequently used for dictation, for querying databases, and for giving commands to computer-based systems, especially in professions that rely on specialized vocabularies.

      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 often 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, 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.

Vladimir Zwass

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Universalium. 2010.

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