THE INFINITE GAUSSIAN MODELS: AN APPLICATION TO SPEAKER IDENTIFICATION

Abstract

When modeling speech with traditional Gaussian Mixture Models (GMM) a major problem is that one need to fix a priori the number of GMMs. Using the infinite version of GMMs allows to overcome this problem. This is based on considering a Dirichlet process with a Bayesian inference via Gibbs sampling rather than the traditional EM inference. The paper investigates the usefulness of the infinite Gaussian modeling using the state of the art SVM classifiers.