Distributed genetic algorithm for Gaussian mixture model based speaker identification

Abstract

This paper presents a novel algorithm for reducing the computational complexity of identifying a speaker within a Gaussian mixture speaker model (GMM) framework. We have combined distributed genetic algorithm (DGA) and the Markov random field (MRF) to avoid typical local minima for speaker vector quantization. To improve the computation efficiency, only unstable chromosomes corresponding to speaker data parts are evolved. Identification accuracies of 93% were achieved for 100 Mandarin speakers. © 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.