Telephone speech recognition based on Bayesian adaptation of hidden Markov models
This paper presents an adaptation method of speech hidden Markov models (HMMs) for telephone speech recognition. Our goal is to automatically adapt the HMM parameters so that the adapted HMM parameters can match with the telephone environment. In this study, two kinds of transformation-based adaptations are investigated. One is the bias transformation and the other is the affine transformation. A Bayesian estimation technique which incorporates prior knowledge into the transformation is applied for estimating the transformation parameters. Experiments show that the proposed approach can be successfully employed for self adaptation as well as supervised adaptation. Besides, the performance of telephone speech recognition using Bayesian adaptation is shown to be superior to that using maximum-likelihood adaptation. The affine transformation is also demonstrated to be significantly better than the bias transformation.
telephone speech recognition hidden markov model affine transformation model adaptation maximum a posteriori estimation