Improved Automatic Lipreading by Multiobjective Optimization of Hidden Markov Models 


Vol. 15,  No. 1, pp. 53-60, Feb.  2008
10.3745/KIPSTB.2008.15.1.53


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  Abstract

This paper proposes a new multiobjective optimization method for discriminative training of hidden Markov models (HMMs) used as the recognizer for automatic lipreading. While the conventional Baum-Welch algorithm for training HMMs aims at maximizing the probability of the data of a class from the corresponding HMM, we define a new training criterion composed of two minimization objectives and develop a global optimization method of the criterion based on simulated annealing. The result of a speaker-dependent recognition experiment shows that the proposed method improves performance by the relative error reduction rate of about 8% in comparison to the Baum-Welch algorithm.

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[IEEE Style]

J. S. Lee and C. H. Park, "Improved Automatic Lipreading by Multiobjective Optimization of Hidden Markov Models," The KIPS Transactions:PartB , vol. 15, no. 1, pp. 53-60, 2008. DOI: 10.3745/KIPSTB.2008.15.1.53.

[ACM Style]

Jong Seok Lee and Cheol Hoon Park. 2008. Improved Automatic Lipreading by Multiobjective Optimization of Hidden Markov Models. The KIPS Transactions:PartB , 15, 1, (2008), 53-60. DOI: 10.3745/KIPSTB.2008.15.1.53.

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