September 22-25, 1997
This paper proposes a Bayesian affine transformation of hidden Markov model (HMM) parameters for reducing the acoustic mismatch problem in telephone speech recognition. Our purpose is to transform the existing HMM parameters into its new version of specific telephone environment using affine function so as to improve the recognition rate. The maximum a posteriori (MAP) estimation which merges the prior statistics into transformation is applied for estimating the transformation parameters. Experiments demonstrate that the proposed Bayesian affine transformation is effective for instantaneous adaptation and supervised adaptation in telephone speech recognition. Model transformation using MAP estimation performs better than that using maximum-likelihood (ML) estimation.
Bibliographic reference. Chien, Jen-Tzung / Wang, Hsiao-Chuan / Lee, Chin-Hui (1997): "Bayesian affine transformation of HMM parameters for instantaneous and supervised adaptation in telephone speech recognition", In EUROSPEECH-1997, 2563-2566.