MODEL SELECTION IN ACOUSTIC MODELING

Abstract

Recently several classes of models have been suggested for use in continuous density HMMs for speech recognition. This paper proposes to choose both the model type and model size (number of parameters) by optimizing the Bayesian information criterion. Specifically we apply this to Gaussian mixture density estimation to determine both the number of Gaussians and the covariance structure of each Gaussian, and decision tree clustering of HMM states. A numerical algorithm similar to the EM algorithm for mixture density estimation is proposed for optimizing BIC.