Adaptive Learning and Compensation of Hidden Markov Model for Robust Speech Recognition

In this report we start with a revisit to the statistical for mulation of the automatic speech recognition ASR prob lem and identify the factors which might in uence the per formance of the conventional plug in MAP decision rule for ASR We summarize our recent research e orts on a class of robust speech recognition problems in which mismatches between training and testing conditions exist but an ac curate knowledge of the mismatch mechanism is unknown The only available information is the test data along with a set of pre trained speech models and the decision parame ters We focus on two types of Bayesian techniques namely on line Bayesian adaptation of hidden Markov model pa rameters and the Bayesian predictive classi cation approach We conclude the report with a brief mention of our ongo ing research e orts towards a robust and intelligent spoken dialogue system