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An N-Best Strategy, Dynamic Grammars and Selectively Trained Neural Networks for Real-Time Recognition of Continuously Spelled Names over the Telephone

Identifieur interne : 00C327 ( Main/Exploration ); précédent : 00C326; suivant : 00C328

An 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. Mari

Source :

RBID : CRIN:junqua95b

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.


Affiliations:

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Le document en format XML

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{{Explor lien
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   |area=    InforLorV4
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   |clé=     CRIN:junqua95b
   |texte=   An N-Best Strategy, Dynamic Grammars and Selectively Trained Neural Networks for Real-Time Recognition of Continuously Spelled Names over the Telephone
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