Speaker Recognition Based on Gaussian Mixture Models Using Variational Bayesian Method

摘要

This paper presents a speaker identification system based on Gaussian Mixture Models (GMM) using the variational Bayesian method. Maximum Likelihood (ML) and Maximum A Posterior (MAP) are well-known methods for estimating GMM parameters. However, the overtraining problem occurs with insufficient data due to a point estimate of model parameters. The Bayesian approach estimates a posterior distribution of model parameters and achieves a robust prediction. To solve complicated integral calculations in the Bayesian approach, the variational Bayesian method has been proposed. This paper investigates the performance of the Bayesian approach in large speaker identification tasks.