Keyword Spotting Based on STM Continuous Speech Recognition System
Identifieur interne : 001A25 ( Crin/Corpus ); précédent : 001A24; suivant : 001A26Keyword Spotting Based on STM Continuous Speech Recognition System
Auteurs : C.-T. Guan ; Y. Gong ; Y.-Q. Fu ; J.-P. HatonSource :
English descriptors
- KwdEn :
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.
Links to Exploration step
CRIN:guan95aLe document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" wicri:score="466">Keyword Spotting Based on STM Continuous Speech Recognition System</title></titleStmt><publicationStmt><idno type="RBID">CRIN:guan95a</idno><date when="1995" year="1995">1995</date><idno type="wicri:Area/Crin/Corpus">001A25</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Keyword Spotting Based on STM Continuous Speech Recognition System</title><author><name sortKey="Guan, C T" sort="Guan, C T" uniqKey="Guan C" first="C.-T." last="Guan">C.-T. Guan</name></author><author><name sortKey="Gong, Y" sort="Gong, Y" uniqKey="Gong Y" first="Y." last="Gong">Y. Gong</name></author><author><name sortKey="Fu, Y Q" sort="Fu, Y Q" uniqKey="Fu Y" first="Y.-Q." last="Fu">Y.-Q. Fu</name></author><author><name sortKey="Haton, J P" sort="Haton, J P" uniqKey="Haton J" first="J.-P." last="Haton">J.-P. Haton</name></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>speech recognition</term></keywords></textClass></profileDesc></teiHeader><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></front></TEI><BibTex type="inproceedings"><ref>guan95a</ref><crinnumber>95-R-275</crinnumber><category>3</category><equipe>RFIA</equipe><author><e>Guan, C.-T.</e><e>Gong, Y.</e><e>Fu, Y.-Q.</e><e>Haton, J.-P.</e></author><title>Keyword Spotting Based on STM Continuous Speech Recognition System</title><booktitle>{Proceedings International Conference on Signal Processing Applications and Technologies, Boston (MA, USA)}</booktitle><year>1995</year><month>oct</month><keywords><e>speech recognition</e></keywords><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.</abstract></BibTex></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Lorraine/explor/InforLorV4/Data/Crin/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 001A25 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Crin/Corpus/biblio.hfd -nk 001A25 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Lorraine
|area= InforLorV4
|flux= Crin
|étape= Corpus
|type= RBID
|clé= CRIN:guan95a
|texte= Keyword Spotting Based on STM Continuous Speech Recognition System
}}
| This area was generated with Dilib version V0.6.33. |  |