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Title: Level-building on AdaBoost HMM classifiers and the application to visual speech processing
Authors: Dong, L.
Foo, S.-W. 
Lian, Y. 
Keywords: Adaptive boosting
Hidden Markov model
Visual speech processing
Issue Date: Nov-2004
Citation: Dong, L.,Foo, S.-W.,Lian, Y. (2004-11). Level-building on AdaBoost HMM classifiers and the application to visual speech processing. IEICE Transactions on Information and Systems E87-D (11) : 2460-2471. ScholarBank@NUS Repository.
Abstract: 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.
Source Title: IEICE Transactions on Information and Systems
ISSN: 09168532
Appears in Collections:Staff Publications

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