Normalized training for HMM-based visual speech recognition

This paper presents an approach to estimating the parameters of continuous density HMMs for visual speech recognition. One of the key issues of image-based visual speech recognition is normalization of lip location and lighting conditions prior to estimating the parameters of HMMs. We presented a no...

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Bibliographic Details
Main Authors: Nankaku, Y., Tokuda, K., Kitamura, T., Kobayashi, T.
Format: Conference Proceeding
Language:English
Subjects:
Data mining
Hidden Markov models
Lips
Maximum likelihood estimation
Mouth
Pixel
Speech recognition
Training data
Vectors
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Summary:This paper presents an approach to estimating the parameters of continuous density HMMs for visual speech recognition. One of the key issues of image-based visual speech recognition is normalization of lip location and lighting conditions prior to estimating the parameters of HMMs. We presented a normalized training method in which the normalization process is integrated in the model training. This paper extends it for contrast normalization in addition to average-intensity and location normalization. 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. Experiments on the M2VTS database show that the recognition performance can be significantly improved by the normalized training.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2000.899338