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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 sub-blocks 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 synthesis rule. The method is evaluated on Bimodal Chinese Audio-Video Database (HIT Bi-CAVDB). Experimental results show that the proposed method gives distinctly superior recognition rate than traditional methods.

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