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<Paper uid="W06-1644">
  <Title>Style &amp; Topic Language Model Adaptation Using HMM-LDA</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Adapting language models across styles and topics, such as for lecture transcription, involves combining generic style models with topic-specific content relevant to the target document. In this work, we investigate the use of the Hidden Markov Model with Latent Dirichlet Allocation (HMM-LDA) to obtain syntactic state and semantic topic assignments to word instances in the training corpus. From these context-dependent labels, we construct style and topic models that better model the target document, and extend the traditional bag-of-words topic models to n-grams. Experiments with static model interpolation yielded a perplexity and relative word error rate (WER) reduction of 7.1% and 2.1%, respectively, over an adapted trigram baseline. Adaptive interpolation of mixture components further reduced perplexity by 9.5% and WER by a modest 0.3%.</Paragraph>
  </Section>
class="xml-element"></Paper>