Application of variational Bayesian estimation and clustering to acoustic model adaptation

We apply Variational Bayesian estimation and clustering for speech recognition (VBEC) to an acoustic model adaptation. VBEC can estimate parameter posteriors even when a model includes hidden variables, by using variational Bayesian approach. In addition, VBEC can select an appropriate model structure in clustering triphone states, according to the amount of available adaptation data. Unlike a conventional Bayesian method such as maximum a posteriori (MAP), VBEC is useful even in the case of small amounts of data, because the amount of data per one Gaussian increases due to the model structure selection, and over-training is suppressed. We conduct an off-line supervised adaptation experiment on isolated word recognition, and show the advantage of the proposed method over the conventional method, especially when dealing with small amounts of adaptation data.