A novel feature-level multiple HMMs classifier for Lipreading based on Ada-Boost Gabor kernels selection
- 10.2991/jcis.2008.74How to use a DOI?
- Lipreading, AdaBoost, Gabor features, HMM
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.
- © 2008, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
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 - Proceedings of the 11th Joint Conference on Information Sciences (JCIS 2008) PB - Atlantis Press SP - 433 EP - 438 SN - 1951-6851 UR - https://doi.org/10.2991/jcis.2008.74 DO - 10.2991/jcis.2008.74 ID - Zhang2008/12 ER -