Comparison of Particle Swarm Optimization and Genetic Algorithm for HMM training

Hidden Markov model (HMM) is the dominant technology in speech recognition. The problem of optimizing model parameters is of great interest to the researchers in this area. The Baum-Welch (BW) algorithm is a popular estimation method due to its reliability and efficiency. However, it is easily trapped in local optimum. Recently, genetic algorithm (GA) and particle swarm optimization (PSO) have attracted considerable attention among various modern heuristic optimization techniques. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their performance. This paper presents the application and performance comparison of PSO and GA for continuous HMM optimization in continuous speech recognition. The experimental results demonstrate that PSO is superior to GA in respect of the recognition performance.