A two-channel training algorithm for hidden Markov model and its application to lip reading
EURASIP Journal on Applied Signal Processing
Lip-reading with discriminative deformable models
Machine Graphics & Vision International Journal
Combining incremental Hidden Markov Model and Adaboost algorithm for anomaly intrusion detection
Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics
A new manifold representation for visual speech recognition
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Comparative analysis of lip features for person identification
Proceedings of the 8th International Conference on Frontiers of Information Technology
ASR based on the analasys of the short-melfrequencycepstra time transform
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Lipreading procedure for liveness verification in video authentication systems
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
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The performance of automatic speech recognition (ASR) system can be significantly enhanced with additional information from visual speech elements such as the movement of lips, tongue, and teeth, especially under noisy environment. In this paper, a novel approach for recognition of visual speech elements is presented. The approach makes use of adaptive boosting (AdaBoost) and hidden Markov models (HMMs) to build an AdaBoost-HMM classifier. The composite HMMs of the AdaBoost-HMM classifier are trained to cover different groups of training samples using the AdaBoost technique and the biased Baum-Welch training method. By combining the decisions of the component classifiers of the composite HMMs according to a novel probability synthesis rule, a more complex decision boundary is formulated than using the single HMM classifier. The method is applied to the recognition of the basic visual speech elements. Experimental results show that the AdaBoost-HMM classifier outperforms the traditional HMM classifier in accuracy, especially for visemes extracted from contexts.