Presentation 1999/11/20
Normalized Training for HMM-Based Automatic Lipreading
Yoshihiko Nankaku, Keiichi Tokuda, Tadashi Kitamura, Takao Kobayashi,
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Abstract(in English) This paper describes an approach to estimating the parameters of continuous density HMMs for visual speech recognition. Methods to extract speech information from image sequences are largely categorized into two approaches: model-based approach and image- or pixel-based approach. One of the key issues of image-based visual speech recognition is normalization of lip location and lighting condition prior to estimating the parameters of HMMs. We present an average-intensity and location normalized training method, in which the normalization process is integrated in the model training. The proposed method provides a theoretically-well-defined algorithm based on a maximum likelihood formulation, hence the likelihood for the training data is guaranteed to increase at each iteration of the normalized training. Experimental results show that the recognition performance is significantly improved by the normalized training.
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Keyword(in English) hidden Markov model / lipreading / normalized training
Paper # PRMU99-158
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Conference Information
Committee PRMU
Conference Date 1999/11/20(1days)
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Registration To Pattern Recognition and Media Understanding (PRMU)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Normalized Training for HMM-Based Automatic Lipreading
Sub Title (in English)
Keyword(1) hidden Markov model
Keyword(2) lipreading
Keyword(3) normalized training
1st Author's Name Yoshihiko Nankaku
1st Author's Affiliation Department of Computer Science, Nagoya Inst. Tech()
2nd Author's Name Keiichi Tokuda
2nd Author's Affiliation Department of Computer Science, Nagoya Inst. Tech
3rd Author's Name Tadashi Kitamura
3rd Author's Affiliation Department of Computer Science, Nagoya Inst. Tech
4th Author's Name Takao Kobayashi
4th Author's Affiliation Interdisciplinary Graduate School of Science and Engineering, Tokyo Inst. Tech.
Date 1999/11/20
Paper # PRMU99-158
Volume (vol) vol.99
Number (no) 450
Page pp.pp.-
#Pages 6
Date of Issue