Bayesian adaptation in HMM training and decoding using a mixture of feature transforms

Abstract

Adaptive training under a Bayesian framework addresses some limitations of the standard maximum likelihood approaches. Also, the adaptively trained system can be directly used in unsupervised inference. The Bayesian framework uses a distribution of the transform rather than a point estimate. A continuous transform distribution makes the integral associated with the Bayesian framework intractable and therefore various approximations have been proposed. In this paper we model the transform distribution via a mixture of transforms. Under this model, the likelihood of an utterance is computed as a weighted sum of the likelihoods obtained by transforming its features based on each of the transforms in the mixture, with weights set to the transform priors. Experimental results on Arabic broadcast news exhibit increased likelihood on acoustic training data and improved speech recognition performance on unseen test data, compared to speaker independent and standard adaptive models.