Base Transformation for Environment Adaptation in Continuous Speech Recognition
Identifieur interne : 001186 ( Crin/Curation ); précédent : 001185; suivant : 001187Base Transformation for Environment Adaptation in Continuous Speech Recognition
Auteurs : Y. GongSource :
English descriptors
Abstract
A specific background noise, speaker or transmission line condition of a speech recognizer is referred as an environment. A mismatch between the training and operating environments can severely degrade recognition accuracy. We present a base transformation method for environment adaptation, which converts an environmental difference into a base difference and reduces the difference by a base transformation. Experiments were conducted on adapting to telephone quality speech, to a new speaker and to speech corrupted by additive Gaussian noise. Using two sentences (5 sec duration) as adaptation data, the method gives a telephone line adapted recognition accuracy of 93.5========percnt; and a speaker adapted accuracy of about 90========percnt;, for a city name recognition task. Using nine sentences (20 sec duration) with SNRs better than 10dB, a noise-adapted recognition accuracy of 90========percnt; was obtained on a 206 word recognition task.
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>base transformation</term>
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<front><div type="abstract" xml:lang="en" wicri:score="3587">A specific background noise, speaker or transmission line condition of a speech recognizer is referred as an environment. A mismatch between the training and operating environments can severely degrade recognition accuracy. We present a base transformation method for environment adaptation, which converts an environmental difference into a base difference and reduces the difference by a base transformation. Experiments were conducted on adapting to telephone quality speech, to a new speaker and to speech corrupted by additive Gaussian noise. Using two sentences (5 sec duration) as adaptation data, the method gives a telephone line adapted recognition accuracy of 93.5========percnt; and a speaker adapted accuracy of about 90========percnt;, for a city name recognition task. Using nine sentences (20 sec duration) with SNRs better than 10dB, a noise-adapted recognition accuracy of 90========percnt; was obtained on a 206 word recognition task.</div>
</front>
</TEI>
<BibTex type="inproceedings"><ref>gong93a</ref>
<crinnumber>93-R-171</crinnumber>
<category>3</category>
<equipe>RFIA</equipe>
<author><e>Gong, Y.</e>
</author>
<title>Base Transformation for Environment Adaptation in Continuous Speech Recognition</title>
<booktitle>{Proceedings 3rd European Conference onSpeech Communication and Technology, Berlin (Germany)}</booktitle>
<year>1993</year>
<volume>3</volume>
<pages>2227-2230</pages>
<month>sep</month>
<keywords><e>environment adaptation</e>
<e>base transformation</e>
<e>noisy speech recognition</e>
</keywords>
<abstract>A specific background noise, speaker or transmission line condition of a speech recognizer is referred as an environment. A mismatch between the training and operating environments can severely degrade recognition accuracy. We present a base transformation method for environment adaptation, which converts an environmental difference into a base difference and reduces the difference by a base transformation. Experiments were conducted on adapting to telephone quality speech, to a new speaker and to speech corrupted by additive Gaussian noise. Using two sentences (5 sec duration) as adaptation data, the method gives a telephone line adapted recognition accuracy of 93.5========percnt; and a speaker adapted accuracy of about 90========percnt;, for a city name recognition task. Using nine sentences (20 sec duration) with SNRs better than 10dB, a noise-adapted recognition accuracy of 90========percnt; was obtained on a 206 word recognition task.</abstract>
</BibTex>
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