Abstract
ABSTRACf We present a mathematical framework for Bayesian adaptive leaming of the param eters of stochastic models. Maximum a posteriori (MAP) estimation algorithms are developed for hidden Markov models and for a number of useful models commonly used in automatic speech recognition and natural language processing. The MAP formulation offers a way to combine ex isting prior knowledge and a smaIl set of newly acquired task-specific data in an optimal manner. It is therefore ideal for adaptive learning applications such as speaker and task adaptation.
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