speech recognition

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speech recognition

[′spēch ‚rek·ig′nish·ən]
(engineering acoustics)
The process of analyzing an acoustic speech signal to identify the linguistic message that was intended, so that a machine can correctly respond to spoken commands.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.

speech recognition

(Or voice recognition) The identification of spoken words by a machine. The spoken words are digitised (turned into sequence of numbers) and matched against coded dictionaries in order to identify the words.

Most systems must be "trained," requiring samples of all the actual words that will be spoken by the user of the system. The sample words are digitised, stored in the computer and used to match against future words. More sophisticated systems require voice samples, but not of every word. The system uses the voice samples in conjunction with dictionaries of larger vocabularies to match the incoming words. Yet other systems aim to be "speaker-independent", i.e. they will recognise words in their vocabulary from any speaker without training.

Another variation is the degree with which systems can cope with connected speech. People tend to run words together, e.g. "next week" becomes "neksweek" (the "t" is dropped). For a voice recognition system to identify words in connected speech it must take into account the way words are modified by the preceding and following words.

It has been said (in 1994) that computers will need to be something like 1000 times faster before large vocabulary (a few thousand words), speaker-independent, connected speech voice recognition will be feasible.
This article is provided by FOLDOC - Free Online Dictionary of Computing (foldoc.org)

voice recognition

(1) Using a person's voice as a form of identification. See two-factor authentication.

(2) The conversion of spoken words into computer text. Speech is first digitized and then matched against a dictionary of coded waveforms. Also called "speech recognition," the matches are converted into text as if the words were typed on the keyboard. "Speaker-dependent" systems require users to enunciate samples to train and fine tune the system. "Speaker-independent" recognition such as telephone voice response systems do not require training but generally handle only a limited vocabulary.

Three Categories
The least taxing on the electronics, "command" systems recognize several dozen words and eliminate using the mouse or keyboard. "Discrete voice" recognition systems used for dictation require a pause between each word. "Continuous voice" recognition understands natural speech without pauses and is the most process intensive. The Holy Grail of voice recognition, speaker-independent, continuous systems that handle extensive vocabularies are slowly but surely becoming mainstream. Contrast with speaker recognition.

First Handheld Speech Recognition
The first continuous dictation in a handheld device was in 2000 when Lernout & Hauspie showed off this Linux PDA prototype. It provided keyboard-free email composition. (Image courtesy of Lernout & Hauspie.)
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References in periodicals archive ?
Real-time frequency-based noise-robust automatic speech recognition using multi-nets artificial neural networks: A multi-views multi-learners approach, Neurocomputing, 129(5), 1053-1063, 2014.
Waibel, "Session independent non-audible speech recognition using surface electromyography," in IEEE Workshop on Automatic Speech Recognition and Understanding, 2005, pp.
(2000) Quantitative assessment of second language learners' fluency by means of automatic speech recognition technology.
Audience S1.0 is based on the company's fourth generation Advanced Voice technology and third generation Automatic Speech Recognition (ASR) Assist technology.
Further, automatic speech recognition (ASR) based CALL systems provide learners with an integrated environment in which they obtain immediate evaluation of their English utterances, and allow them to practice at any time to enhance their speaking (Chiu, Liou, & Yeh, 2007; Chen, 2011).
Techniques for noise robustness in automatic speech recognition.
When related to the conversion of a spoken utterance to a string of lexical words only, it is referred to as automatic speech recognition (ASR).
Technology including automatic speech recognition and machine translators can make human translators more effective, he says.
It is therefore worth to look at the promising automatic speech recognition (ASR) technology to provide such intervention.
Audio-visual speech recognition has attracted substantial interest in recent years since it was demonstrated that visual information regarding lip dynamics improves the performance of automatic speech recognition systems.
Fastlink, part of Mobile Telecommunications Company (MTC) Group, announced Saturday the launch of the Automatic Speech Recognition (ASR) system.

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