Particle Swarm Optimization for Feature Selection in Speaker Verification

The problem addressed in this paper concerns the feature subset selection for an automatic speaker verification system. An effective algorithm based on particle swarm optimization is proposed here for discovering the best feature combinations. After feature reduction phase, feature vectors are applied to a Gaussian mixture model which is a text-independent speaker verification model. The performance of proposed system is compared to the performance of a genetic algorithm-based system and the baseline algorithm. Experimentation is carried out, using TIMIT corpora. The results of experiments indicate that with the optimized feature subset, the performance of the system is improved. Moreover, the speed of verification is significantly increased since by use of PSO, number of features is reduced over 85% which consequently decrease the complexity of our ASV system.