Online Bayesian tree-structured transformation of HMMs with optimal model selection for speaker adaptation

Abstract

This paper presents a new recursive Bayesian learning approach for transformation parameter estimation in speaker adaptation. Our goal is to incrementally transform or adapt a set of hidden Markov model (HMM) parameters for a new speaker and gain large performance improvement from a small amount of adaptation data. By constructing a clustering tree of HMM Gaussian mixture components, the linear regression (LR) or affine transformation parameters for HMM Gaussian mixture components are dynamically searched. An online Bayesian learning technique is proposed for recursive maximum a posteriori (MAP) estimation of LR and affine transformation parameters. This technique has the advantages of being able to accommodate flexible forms of transformation functions as well as a priori probability density functions (PDFs). To balance between model complexity and goodness of fit to adaptation data, a dynamic programming algorithm is developed for selecting models using a Bayesian variant of the "minimum description length" (MDL) principle. Speaker adaptation experiments with a 26-letter English alphabet vocabulary were conducted, and the results confirmed effectiveness of the online learning framework.