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Recognition of visual speech elements using adaptively boosted hidden Markov models.

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Recognition of visual speech elements using adaptively boosted hidden Markov models.

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Title: Recognition of visual speech elements using adaptively boosted hidden Markov models.
Author: Foo, Say Wei.; Lian, Yong.; Dong, Liang.
Copyright year: 2004
Abstract: 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.
Type: Journal Article
Series/ Journal Title: IEEE transactions on circuits and systems for video technology
Rights: © 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Version: Published version

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