11th Joint International Conference on Information Sciences

A novel feature-level multiple HMMs classifier for Lipreading based on Ada-Boost Gabor kernels selection

Authors
Shengping Zhang 0, Hongxun Yao
Corresponding Author
Shengping Zhang
0Harbin Institute of Technology
Available Online December 2008.
DOI
https://doi.org/10.2991/jcis.2008.74How to use a DOI?
Keywords
Lipreading, AdaBoost, Gabor features, HMM
Abstract
In this paper, a novel feature-level Multiple HMMs classifier for lipreading is presented. Firstly, it subdivides mouth images into four non-overlapping subblocks. Then AdaBoost is used to adaptively select optimal Gabor kernels from four subblocks convolved with different Gabor kernel functions and corresponding HMMs are trained. Finally the “boosted” HMMs are used to build a stronger multiple HMMs classifier by combining the decisions of the composite HMMs according to a probability synthe-sis rule. The method is evaluated on Bi-modal Chinese Audio-Video Database (HIT Bi-CAVDB). Experimental results show that the proposed method gives distinctly superior recognition rate than traditional methods.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
11th Joint International Conference on Information Sciences
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2008
ISBN
978-90-78677-18-5
ISSN
1951-6851
DOI
https://doi.org/10.2991/jcis.2008.74How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Shengping Zhang
AU  - Hongxun Yao
PY  - 2008/12
DA  - 2008/12
TI  - A novel feature-level multiple HMMs classifier for Lipreading based on Ada-Boost Gabor kernels selection
BT  - 11th Joint International Conference on Information Sciences
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2008.74
DO  - https://doi.org/10.2991/jcis.2008.74
ID  - Zhang2008/12
ER  -