Batch, incremental and instantaneous adaptation techniques for speech recognition

We present a framework for maximum a posteriori adaptation of large scale HMM speech recognizers. In this framework, we introduce mechanisms that take advantage of correlations present among HMM parameters in order to maximize the number of parameters that can be adapted by a limited number of observations. We are also separately exploring the feasibility of instantaneous adaptation techniques. Instantaneous adaptation attempts to improve recognition on a single sentence, the same sentence that is used to estimate the adaptation. We show that sizable gains (20-40% reduction in error rate) can be achieved by either batch or incremental adaptation for large vocabulary recognition of native speakers. The same techniques cut the error rate for recognition of non-native speakers by factors of 2 to 4, bringing their performance much closer to the native speaker performance. We also demonstrate that good improvements in performance (25-30%) are realized when instantaneous adaptation is used for recognition of non-native speakers