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Plausibility Functions in Continuous Speech Recognition : {The} {VINICS} System

Identifieur interne : 001225 ( Crin/Curation ); précédent : 001224; suivant : 001226

Plausibility Functions in Continuous Speech Recognition : {The} {VINICS} System

Auteurs : Y. Gong ; J.-P. Haton

Source :

RBID : CRIN:gong93d

English descriptors

Abstract

We propose a new approach to phoneme-based continuous speech recognition when a time function of plausibility of observing each phoneme is given. We introduce a criterion for best sentence, related to the sum of plausibilities of individual symbols composing the sentence. Based on the idea of making use of a high plausibility region to reduce the computation load while keeping optimality, our method finds the most plausible sentences relating to the input speech, given the plausibility \mua,n of observing each phoneme a at each time slot n. Two optimization procedures are defined to deal with the following embedded search processes\, : (1) find the best path connecting peaks of the plausibility functions of two successive symbols, and (2) find the best time transition slot index for two given peaks. Dynamic programming is used in these two procedures. Since the best path finding algorithm does not search slot by slot, the recognition is highly efficient. Experimental results with the VINICS system show that the method gives a better recognition precision while requiring about 1/20 computing time, compared to traditional DP based methods. The experimental system obtained a 95========percnt; sentence recognition rate on a speaker-dependent test.

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Le document en format XML

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<div type="abstract" xml:lang="en" wicri:score="4626">We propose a new approach to phoneme-based continuous speech recognition when a time function of plausibility of observing each phoneme is given. We introduce a criterion for best sentence, related to the sum of plausibilities of individual symbols composing the sentence. Based on the idea of making use of a high plausibility region to reduce the computation load while keeping optimality, our method finds the most plausible sentences relating to the input speech, given the plausibility \mua,n of observing each phoneme a at each time slot n. Two optimization procedures are defined to deal with the following embedded search processes\, : (1) find the best path connecting peaks of the plausibility functions of two successive symbols, and (2) find the best time transition slot index for two given peaks. Dynamic programming is used in these two procedures. Since the best path finding algorithm does not search slot by slot, the recognition is highly efficient. Experimental results with the VINICS system show that the method gives a better recognition precision while requiring about 1/20 computing time, compared to traditional DP based methods. The experimental system obtained a 95========percnt; sentence recognition rate on a speaker-dependent test.</div>
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<crinnumber>93-R-229</crinnumber>
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<author>
<e>Gong, Y.</e>
<e>Haton, J.-P.</e>
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<title>Plausibility Functions in Continuous Speech Recognition : {The} {VINICS} System</title>
<journal>Speech Communication</journal>
<year>1993</year>
<volume>13</volume>
<number>1-2</number>
<pages>187-196</pages>
<month>Oct</month>
<keywords>
<e>speech recognition</e>
<e>neural networks</e>
<e>phoneme recognition</e>
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<abstract>We propose a new approach to phoneme-based continuous speech recognition when a time function of plausibility of observing each phoneme is given. We introduce a criterion for best sentence, related to the sum of plausibilities of individual symbols composing the sentence. Based on the idea of making use of a high plausibility region to reduce the computation load while keeping optimality, our method finds the most plausible sentences relating to the input speech, given the plausibility \mua,n of observing each phoneme a at each time slot n. Two optimization procedures are defined to deal with the following embedded search processes\, : (1) find the best path connecting peaks of the plausibility functions of two successive symbols, and (2) find the best time transition slot index for two given peaks. Dynamic programming is used in these two procedures. Since the best path finding algorithm does not search slot by slot, the recognition is highly efficient. Experimental results with the VINICS system show that the method gives a better recognition precision while requiring about 1/20 computing time, compared to traditional DP based methods. The experimental system obtained a 95========percnt; sentence recognition rate on a speaker-dependent test.</abstract>
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