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Automatic visual lipreading, an efficient and convenient way of human–machine interaction, recognizes the content of the conversation from dynamic visual features of the speakers. Automatic lipreading based on acoustic speech alone can effectively prevent interference in complex environment, particularly under noisy conditions. In this paper, we propose novel visual extraction and HMM classification methods for automatic lipreading system, which reduce the dimension by using locally sensitive discriminant analysis algorithm and quantitative cluster by K-means algorithm. A model-based hybrid feature extraction method is proposed by optimizing the weight matrix of the LSDA algorithm. The effectiveness of the suggested approach is demonstrated by preliminary experiments on the English video database. Experimental results demonstrate that the proposed optimized algorithm can increase recognition rate up to 97%, which is 18% higher than the original algorithm. [ABSTRACT FROM AUTHOR]