Adaptation of hidden Markov models using multiple stochastic transformations

The recognition accuracy in recent large vocabulary Automatic Speech Recognition (ASR) systems is highly related to the existing mismatch between the training and test sets. For example, dialect di erences across the training and testing speakers result to a signi cant degradation in recognition performance. Some popular adaptation approaches improve the recognition performance of speech recognizers based on hidden Markov models with continuous mixture densities by using linear transforms to adapt the means, and possibly the covariances of the mixture Gaussians. In this paper, we propose a novel adaptation technique that adapts the means and, optionally, the covariances of the mixture Gaussians by using multiple stochastic transformations. We perform both speaker and dialect adaptation experiments, and we show that our method signi cantly improves the recognition accuracy and the robustness of our system. The experiments are carried out with SRI's DECIPHER speech recognition system.