关键词: 说话人识别, 微粒群算法, 高斯混合模型

Abstract: The traditional training methods of Gaussian Mixture Model (GMM) are sensitive to the initial model parameters, which often leads to a local optimal parameter in practice. To resolve this problem, a new GMM optimization method was proposed based on Particle Swarm Optimization (PSO). It utilized Maximum Likelihood (ML) algorithm in the PSO iteration and provided a new architecture of hybrid algorithm. Because of the global optimization characteristic of the particle swarm optimizer method and the strong local searching capacity of ML, it can obtain model parameters with high precision. Experiment for text-independent speaker identification shows that this method can obtain more optimum GMM parameters and better results than the traditional method.

Key words: speaker identification, Particle Swarm Optimization (PSO), Gaussian Mixture Model (GMM)