Dynamic Bayesian Networks for Automatic Speech Recognition

Murat Deviren 1
1 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : State-of-the-art automatic speech recognition (ASR) systems are based on probabilistic modelling of the speech signal using Hidden Markov Models. The limitations of these systems under real life conditions arose a question about the robustness of the underlying acoustic modelling methodology. The scope of my thesis is to explore the formalism of Probabilistic Graphical Models, particularly Dynamic Bayesian Networks, from a theoretical and practical point of view, with the aim of developing reliable models of speech and of developing robust ASR systems.
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Communication dans un congrès
Eighteenth National Conference on Artificial Intelligence, AAAI 2002, SIGART/AAAI Doctoral Consortium, Jul 2002, Edmonton, Alberta, Canada, 1 p, 2002
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https://hal.inria.fr/inria-00100856
Contributeur : Publications Loria <>
Soumis le : mardi 26 septembre 2006 - 14:52:29
Dernière modification le : jeudi 11 janvier 2018 - 06:19:55

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  • HAL Id : inria-00100856, version 1

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Murat Deviren. Dynamic Bayesian Networks for Automatic Speech Recognition. Eighteenth National Conference on Artificial Intelligence, AAAI 2002, SIGART/AAAI Doctoral Consortium, Jul 2002, Edmonton, Alberta, Canada, 1 p, 2002. 〈inria-00100856〉

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