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Level-Building on AdaBoost HMM Classifiers and the Application to Visual Speech Processing
Liang DONG Say-Wei FOO Yong LIAN
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2004/11/01
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Speech and Hearing
hidden Markov model, adaptive boosting, level-building, visual speech processing,
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The Hidden Markov Model (HMM) is a popular statistical framework for modeling and analyzing stochastic signals. In this paper, a novel strategy is proposed that makes use of level-building algorithm with a chain of AdaBoost HMM classifiers to model long stochastic processes. AdaBoost HMM classifier belongs to the class of multiple-HMM classifier. It is specially trained to identify samples with erratic distributions. By connecting the AdaBoost HMM classifiers, processes of arbitrary length can be modeled. A probability trellis is created to store the accumulated probabilities, starting frames and indices of each reference model. By backtracking the trellis, a sequence of best-matched AdaBoost HMM classifiers can be decoded. The proposed method is applied to visual speech processing. A selected number of words and phrases are decomposed into sequences of visual speech units using both the proposed strategy and the conventional level-building on HMM method. Experimental results show that the proposed strategy is able to more accurately decompose words/phrases in visual speech than the conventional approach.