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http://dx.doi.org/10.3745/KIPSTB.2008.15-B.1.53

Improved Automatic Lipreading by Multiobjective Optimization of Hidden Markov Models  

Lee, Jong-Seok (한국과학기술원 전자전산학부)
Park, Cheol-Hoon (한국과학기술원 전자전산학부)
Publication Information
The KIPS Transactions:PartB / v.15B, no.1, 2008 , pp. 53-60 More about this Journal
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.
Keywords
Automatic Lipreading; Multiobjective Optimization; Discriminative Training; Simulated Annealing;
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