Discriminative training of hidden Markov models by multiobjective optimization for visual speech recognition

This paper proposes a novel discriminative training algorithm of hidden Markov models (HMMs) based on the multiobjective optimization for visual speech recognition. We develop a new criterion composed of two minimization objectives for training HMMs discriminatively and a global multiobjective optimization algorithm based on the simulated annealing algorithm to find the Pareto solutions of the optimization problem. We demonstrate the effectiveness of the proposed method via an isolated digit recognition experiment. The results show that the proposed method is superior to the conventional maximum likelihood estimation and the popular discriminative training algorithms.