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 expectationmaximization 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.
|Number of pages||9|
|Journal||IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics|
|Publication status||Published - 2010 Aug|
Bibliographical noteFunding Information:
Manuscript received April 27, 2007; revised February 3, 2008; accepted April 2, 2008. Date of publication January 8, 2010; date of current version July 16, 2010. This work was supported in part by Grant R01-2003-000-10829-0 from the Basic Research Program of the Korea Science and Engineering Foundation and by the Brain Korea 21 Project from the School of Information Technology, KAIST. This paper was recommended by Associate Editor S. Hu.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Information Systems
- Human-Computer Interaction
- Computer Science Applications
- Electrical and Electronic Engineering