Combined Acoustic and Pronunciation Modelling for Non-Native Speech Recognition

Ghazi Bouselmi 1 Dominique Fohr 1 Irina Illina 1
1 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In this paper, we present several adaptation methods for non-native speech recognition. We have tested pronunciation modelling, MLLR and MAP non-native pronunciation adaptation and HMM models retraining on the HIWIRE foreign accented English speech database. The ``phonetic confusion'' scheme we have developed consists in associating to each spoken phone several sequences of confused phones. In our experiments, we have used different combinations of acoustic models representing the canonical and the foreign pronunciations: spoken and native models, models adapted to the non-native accent with MAP and MLLR. The joint use of pronunciation modelling and acoustic adaptation led to further improvements in recognition accuracy. The best combination of the above mentioned techniques resulted in a relative word error reduction ranging from 46% to 71%.
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Communication dans un congrès
Universiteit antwerpen and Radboud University Nijmegen and Katholieke Universiteit Leuven. InterSpeech 2007, Aug 2007, Antwerp, Belgium. ISCA, 2007
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Dernière modification le : jeudi 11 janvier 2018 - 06:19:56
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  • HAL Id : inria-00184560, version 1
  • ARXIV : 0711.0811

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Ghazi Bouselmi, Dominique Fohr, Irina Illina. Combined Acoustic and Pronunciation Modelling for Non-Native Speech Recognition. Universiteit antwerpen and Radboud University Nijmegen and Katholieke Universiteit Leuven. InterSpeech 2007, Aug 2007, Antwerp, Belgium. ISCA, 2007. 〈inria-00184560〉

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