An N-Best Strategy, Dynamic Grammars and Selectively Trained Neural Networks for Real-Time Recognition of Continuously Spelled Names over the Telephone
Identifieur interne : 001A28 ( Crin/Curation ); précédent : 001A27; suivant : 001A29An N-Best Strategy, Dynamic Grammars and Selectively Trained Neural Networks for Real-Time Recognition of Continuously Spelled Names over the Telephone
Auteurs : J.-C. Junqua ; S. Valente ; D. Fohr ; J.-F. MariSource :
Abstract
We introduce SmarTspelL, a new speaker-independent algorithm to recognize continuously spelled names over the telephone. Our method is based on an N-best multi-pass recognition strategy applying costly constraints when the number of possible candidates is low. This strategy outperforms an HMM recognizer using a grammar containing all the possible names. It is also more suitable to real-time. For a 3,388 name dictionary, a 95.3% name recognition rate is obtained. A real-time prototype has been implemented on a workstation. We also present comparisons of different feature sets for speech representation, and two speech recognition approaches based on first- and second- order HMMs.
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" wicri:score="518">An N-Best Strategy, Dynamic Grammars and Selectively Trained Neural Networks for Real-Time Recognition of Continuously Spelled Names over the Telephone</title></titleStmt><publicationStmt><idno type="RBID">CRIN:junqua95b</idno><date when="1995" year="1995">1995</date><idno type="wicri:Area/Crin/Corpus">001A28</idno><idno type="wicri:Area/Crin/Curation">001A28</idno><idno type="wicri:explorRef" wicri:stream="Crin" wicri:step="Curation">001A28</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">An N-Best Strategy, Dynamic Grammars and Selectively Trained Neural Networks for Real-Time Recognition of Continuously Spelled Names over the Telephone</title><author><name sortKey="Junqua, J C" sort="Junqua, J C" uniqKey="Junqua J" first="J.-C." last="Junqua">J.-C. Junqua</name></author><author><name sortKey="Valente, S" sort="Valente, S" uniqKey="Valente S" first="S." last="Valente">S. Valente</name></author><author><name sortKey="Fohr, D" sort="Fohr, D" uniqKey="Fohr D" first="D." last="Fohr">D. Fohr</name></author><author><name sortKey="Mari, J F" sort="Mari, J F" uniqKey="Mari J" first="J.-F." last="Mari">J.-F. Mari</name></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en" wicri:score="2114">We introduce SmarTspelL, a new speaker-independent algorithm to recognize continuously spelled names over the telephone. Our method is based on an N-best multi-pass recognition strategy applying costly constraints when the number of possible candidates is low. This strategy outperforms an HMM recognizer using a grammar containing all the possible names. It is also more suitable to real-time. For a 3,388 name dictionary, a 95.3% name recognition rate is obtained. A real-time prototype has been implemented on a workstation. We also present comparisons of different feature sets for speech representation, and two speech recognition approaches based on first- and second- order HMMs.</div></front></TEI><BibTex type="inproceedings"><ref>junqua95b</ref><crinnumber>95-R-278</crinnumber><category>3</category><equipe>RFIA</equipe><author><e>Junqua, J.-C.</e><e>Valente, S.</e><e>Fohr, D.</e><e>Mari, J.-F.</e></author><title>An N-Best Strategy, Dynamic Grammars and Selectively Trained Neural Networks for Real-Time Recognition of Continuously Spelled Names over the Telephone</title><booktitle>{Proceedings IEEE International Conference on Acoustics, Speech and Signal Processing, Detroit (Michigan, USA)}</booktitle><year>1995</year><pages>852-855</pages><month>may</month><abstract>We introduce SmarTspelL, a new speaker-independent algorithm to recognize continuously spelled names over the telephone. Our method is based on an N-best multi-pass recognition strategy applying costly constraints when the number of possible candidates is low. This strategy outperforms an HMM recognizer using a grammar containing all the possible names. It is also more suitable to real-time. For a 3,388 name dictionary, a 95.3% name recognition rate is obtained. A real-time prototype has been implemented on a workstation. We also present comparisons of different feature sets for speech representation, and two speech recognition approaches based on first- and second- order HMMs.</abstract></BibTex></record>Pour manipuler ce document sous Unix (Dilib)
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