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The automatic speech recognition engine ESPERE : experiments on telephone speech

Identifieur interne : 001A15 ( Crin/Checkpoint ); précédent : 001A14; suivant : 001A16

The automatic speech recognition engine ESPERE : experiments on telephone speech

Auteurs : Dominique Fohr ; Odile Mella ; Christophe Antoine

Source :

RBID : CRIN:fohr00a

English descriptors

Abstract

This paper presents our automatic speech recognition engine ESPERE and several results obtained from experiments on telephone speech. ESPERE (Engine for SPEech REcognition) is a HMM-based toolbox for speech recognition allowing the user to choose the modeled unit (word, phone, triphone), define the topology of every Hidden Markov Model, train the models with the Baum-Welch algorithm and evaluate the recognition accuracy on speech databases. To validate the ESPERE toolbox, we have conducted tests on real world data : the recognition of a three-digit code to access a call center. We have investigated the influence of some parameters and some preprocessing algorithms. Finally, combining the best parameters, the recognition score reaches 96.4% at the word level and 92.1% at the sentence level.

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CRIN:fohr00a

Le document en format XML

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<div type="abstract" xml:lang="en" wicri:score="2405">This paper presents our automatic speech recognition engine ESPERE and several results obtained from experiments on telephone speech. ESPERE (Engine for SPEech REcognition) is a HMM-based toolbox for speech recognition allowing the user to choose the modeled unit (word, phone, triphone), define the topology of every Hidden Markov Model, train the models with the Baum-Welch algorithm and evaluate the recognition accuracy on speech databases. To validate the ESPERE toolbox, we have conducted tests on real world data : the recognition of a three-digit code to access a call center. We have investigated the influence of some parameters and some preprocessing algorithms. Finally, combining the best parameters, the recognition score reaches 96.4% at the word level and 92.1% at the sentence level.</div>
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<title>The automatic speech recognition engine ESPERE : experiments on telephone speech</title>
<booktitle>{ICSLP, Pékin, China}</booktitle>
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<e>speech recognition</e>
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<abstract>This paper presents our automatic speech recognition engine ESPERE and several results obtained from experiments on telephone speech. ESPERE (Engine for SPEech REcognition) is a HMM-based toolbox for speech recognition allowing the user to choose the modeled unit (word, phone, triphone), define the topology of every Hidden Markov Model, train the models with the Baum-Welch algorithm and evaluate the recognition accuracy on speech databases. To validate the ESPERE toolbox, we have conducted tests on real world data : the recognition of a three-digit code to access a call center. We have investigated the influence of some parameters and some preprocessing algorithms. Finally, combining the best parameters, the recognition score reaches 96.4% at the word level and 92.1% at the sentence level.</abstract>
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