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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<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>
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<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>
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