The automatic speech recognition engine ESPERE : experiments on telephone speech
Identifieur interne : 00A049 ( Main/Merge ); précédent : 00A048; suivant : 00A050The automatic speech recognition engine ESPERE : experiments on telephone speech
Auteurs : Dominique Fohr ; Odile Mella ; Christophe AntoineSource :
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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.
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<author><name sortKey="Mella, Odile" sort="Mella, Odile" uniqKey="Mella O" first="Odile" last="Mella">Odile Mella</name>
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<front><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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