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<Paper uid="H92-1036">
  <Title>MAP Estimation of Continuous Density HMM : Theory and Applications</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
ABSTRACT
</SectionTitle>
    <Paragraph position="0"> We discuss maximum a posteriori estimation of continuous density hidden Markov models (CDHMM). The classical MLE reestimation algorithms, namely the forward-backward algorithm and the segmental k-means algorithm, are expanded and reestimation formulas are given for HMM with Gaussian mixture observation densities. Because of its adaptive nature, Bayesian learning serves as a unified approach for the following four speech recognition applications, namely parameter smoothing, speaker adaptation, speaker group modeling and corrective ~aining. New experimental results on all four applications are provided to show the effectiveness of the MAP estimation approach.</Paragraph>
  </Section>
class="xml-element"></Paper>