Keyword Spotting Based on STM Continuous Speech Recognition System
Identifieur interne : 00CC01 ( Main/Merge ); précédent : 00CC00; suivant : 00CC02Keyword Spotting Based on STM Continuous Speech Recognition System
Auteurs : C.-T. Guan ; Y. Gong ; Y.-Q. Fu ; Jean-Paul Haton [France]Source :
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Abstract
A keyword spotting system using the VINICS continuous speech recognition system based upon the recently proposed Stochastic Trajectory Modeling (STM) is presented. This keyword spotting system is a two pass classifier. The first component is a continuous speech recognizer serving as a word spotter using STM phonemic models. The second component is a postprocessor which rejects non-keywords in the framework of Linear Discrimination (LD) classification. Segmental probability scores of the spotter are also used and can improve the overall performances. Experiments were carried out on a speaker-dependent French database. An average FOM of 77.25========percnt; was obtained. This keyword spotting system is essentially vocabulary-independent and context-independent. New keywords can be added into the vocabulary in a very easy and flexible way.
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<author><name sortKey="Gong, Y" sort="Gong, Y" uniqKey="Gong Y" first="Y." last="Gong">Y. Gong</name>
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<author><name sortKey="Fu, Y Q" sort="Fu, Y Q" uniqKey="Fu Y" first="Y.-Q." last="Fu">Y.-Q. Fu</name>
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<author><name sortKey="Haton, J P" sort="Haton, J P" uniqKey="Haton J" first="J.-P." last="Haton">Jean-Paul Haton</name>
<affiliation><country>France</country>
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<orgName type="laboratoire" n="5">Laboratoire lorrain de recherche en informatique et ses applications</orgName>
<orgName type="university">Université de Lorraine</orgName>
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<front><div type="abstract" xml:lang="en" wicri:score="1890">A keyword spotting system using the VINICS continuous speech recognition system based upon the recently proposed Stochastic Trajectory Modeling (STM) is presented. This keyword spotting system is a two pass classifier. The first component is a continuous speech recognizer serving as a word spotter using STM phonemic models. The second component is a postprocessor which rejects non-keywords in the framework of Linear Discrimination (LD) classification. Segmental probability scores of the spotter are also used and can improve the overall performances. Experiments were carried out on a speaker-dependent French database. An average FOM of 77.25========percnt; was obtained. This keyword spotting system is essentially vocabulary-independent and context-independent. New keywords can be added into the vocabulary in a very easy and flexible way.</div>
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