A Novel Shuffled Particle-pair Optimizer for Speaker Recognition

A novel Shuffled Particle-Pair Optimizer (SPPO) is proposed for speaker recognition based on vector quantization, which combines the advantage both in Particle Swarm Optimization (PSO) and Shuffled Frog-Leaping Algorithm (SFLA). The SPPO contains elements of local exploration and global information exchange to get global optimized speaker codebook. In this algorithm, the population is partitioned into 3 particle-pairs according to the performance, and each particle-pair consists of two particles. The particle-pairs perform simultaneously local exploration using basic operations of PSO (velocity updating and position updating) and LBG algorithm in sequence. A shufflingstrategy, in which the particles are periodically shuffled and reorganized into new particle-pairs, allows for the exchange of information between particle-pairs to move toward the global optimum. Experimental results demonstrat that the performance of this new method is much better than that of LBG, FCM, FRLVQ-FVQ, and PSO consistently with lower speaker recognition error rates, shorter computational time and higher convergence rate. The dependence of the final codebook on the selection of the initial codebook is also reduced effectively.