Speaker adaptation using combined transformation and Bayesian methods

The performance and robustness of a speech recognition system can be improved by adapting the speech models to the speaker, the channel and the task. In continuous mixture-density hidden Markov models the number of component densities is typically very large, and it may not be feasible to acquire a large amount of adaptation data for robust maximum-likelihood estimates. To solve this problem, we propose a constrained estimation technique for Gaussian mixture densities, and combine it with Bayesian techniques to improve its asymptotic properties. We evaluate our algorithms on the large-vocabulary Wall Street Journal corpus for nonnative speakers of American English. The recognition error rate is comparable to the speaker-independent accuracy achieved for native speakers