A New Family of Extended Baum-Welch Update Rules
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چکیده
In this paper, we consider a generalization of the state-of-art discriminative method for optimizing the conditional likelihood in Hidden Markov Models (HMMs), called the Extended Baum-Welch (EBW) algorithm, that has had significant impact on the speech recognition community. We propose a generalized form of EBW update rules that can be associated with a weighted sum of updated and initial models, and demonstrate that using novel update rules can significantly speed up parameter estimation for Gaussian mixtures.
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تاریخ انتشار 2008