Training hidden Markov models by hybrid simulated annealing for visual speech recognition

This paper presents a novel training algorithm of hidden Markov models (HMMs) for visual speech recognition based on a modified simulated annealing (SA) algorithm, hybrid simulated annealing, where SA is combined with a local optimization technique to improve the convergence speed and the solution quality. While the popular training method of HMMs, the expectation-maximization (EM) algorithm, only achieves local optima in the parameter space, the proposed algorithm performs global search and thus obtains solutions giving improved recognition performance. The effectiveness of the proposed method is demonstrated via isolated word recognition experiments.