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

The main core of HMM-based speech recognition systems is Viterbi algorithm. Viterbi algorithm uses dynamic programming to find out the best alignment between the input speech and a 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. Several methods are introduced and evaluated for the representation of particles and their corresponding movement structures. In addition, two segmentation strategies are explored. The first method is the standard segmentation which tries to maximize the likelihood function for each competing acoustic model separately. In the next method, a global segmentation tied between several models and the system tries to optimize the likelihood using a common tied segmentation. 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 and phone classification tasks and shows its significant performance in both accuracy and computational complexity aspects.