A novel approach to HMM-based speech recognition system using particle swarm optimization

The main core of HMM-based speech recognition systems is the Viterbi Algorithm. Viterbi is performed using dynamic programming to find out the best alignment between input speech and given speech model. In this paper, dynamic programming is replaced by a search method which is based on particle swarm optimization algorithm. The major idea is focused on generating an initial population of segmentation vectors in the solution search space and improving the location of segments by an updating algorithm. Two methods are introduced for representation of each particle and movement structure. The results show that the effect of these factors is noticeable in finding the global optimum while maintaining the system accuracy. The idea was tested on an isolated word recognition task and shows its significant performance in both accuracy and computational complexity aspects.