Hybrid Simulated Annealing and Its Application to Optimization of Hidden Markov Models for Visual Speech Recognition

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We propose a novel stochastic optimization algorithm, hybrid simulated annealing (SA), to train hidden Markov models (HMMs) for visual speech recognition. In our algorithm, SA is combined with a local optimization operator that substitutes a better solution for the current one to improve the convergence speed and the quality of solutions. We mathematically prove that the sequence of the objective values converges in probability to the global optimum in the algorithm. The algorithm is applied to train HMMs that are used as visual speech recognizers. While the popular training method of HMMs, the expectation-maximization algorithm, achieves only local optima in the parameter space, the proposed method can perform global optimization of the parameters of HMMs and thereby obtain solutions yielding improved recognition performance. The superiority of the proposed algorithm to the conventional ones is demonstrated via isolated word recognition experiments.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2010-08
Language
English
Article Type
Article
Keywords

BAUM-WELCH; ALGORITHM

Citation

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, v.40, no.4, pp.1188 - 1196

ISSN
1083-4419
DOI
10.1109/TSMCB.2009.2036753
URI
http://hdl.handle.net/10203/96992
Appears in Collection
EE-Journal Papers(저널논문)
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