Adaptation MLLR pour des HMMs
Identifieur interne : 009A90 ( Main/Merge ); précédent : 009A89; suivant : 009A91Adaptation MLLR pour des HMMs
Auteurs : Fabrice Lauri [France] ; Irina Illina [France] ; Dominique Fohr [France]Source :
Descripteurs français
Abstract
Nous présentons dans cet article la technique d'adaptation Maximum Likelihood Linear Regression (MLLR). MLLR permet d'adapter les paramètres des modèles acoustiques d'un système indépendant du locuteur afin d'améliorer la reconnaissance pour un nouveau locuteur ou pour un nouvel environnement de test. Nous avons expérimenté MLLR à des HMMs du moteur ESPERE appris sur le corpus de parole Resource Management (RM). Les résultats montrent une réduction de 12,6% d'erreurs en moyenne par rapport au système indépendant du locuteur, en utilisant 30 phrases d'adaptation.
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Hal:inria-00147543Le document en format XML
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<profileDesc><textClass><keywords scheme="mix" xml:lang="fr"><term>acoustic models</term>
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<front><div type="abstract" xml:lang="fr">Nous présentons dans cet article la technique d'adaptation Maximum Likelihood Linear Regression (MLLR). MLLR permet d'adapter les paramètres des modèles acoustiques d'un système indépendant du locuteur afin d'améliorer la reconnaissance pour un nouveau locuteur ou pour un nouvel environnement de test. Nous avons expérimenté MLLR à des HMMs du moteur ESPERE appris sur le corpus de parole Resource Management (RM). Les résultats montrent une réduction de 12,6% d'erreurs en moyenne par rapport au système indépendant du locuteur, en utilisant 30 phrases d'adaptation.</div>
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