A Maximum Likelihood Approach to Unsupervised Online Adaptation of Stochastic Vector Mapping Function for Robust Speech Recognition

In the past several years, we\´ve been studying feature transformation approaches for robust automatic speech recognition (ASR) based on the concept of stochastic vector mapping (SVM) to compensating for possible "distortions" caused by factors irrelevant to phonetic classification in both training and recognition stages. Although we have demonstrated the usefulness of the SVM-based approaches for several robust ASR applications where diversified yet representative training data are available, the performance improvement of SVM-based approaches is less significant when there is a severe mismatch between training and testing conditions. In this paper, we present a maximum likelihood approach to unsupervised online adaptation (OLA) of SVM function parameters on an utterance-by-utterance basis for achieving further performance improvement. Its effectiveness is confirmed by evaluation experiments on Finnish AuroraS database.