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<Paper uid="P98-2155">
  <Title>Constituent-based Accent Prediction</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Near-perfect automatic accent assignment is attainable for citation-style speech, but better computational models are needed to predict accent in extended, spontaneous discourses. This paper presents an empirically motivated theory of the discourse focusing nature of accent in spontaneous speech. Hypotheses based on this theory lead to a new approach to accent prediction, in which patterns of deviation from citation form accentuation, defined at the constituent or noun phrase level, are atttomatically learned from an annotated corpus. Machine learning experiments on 1031 noun phrases from eighteen spontaneous direction-giving monologues show that accent assignment can be significantly improved by up to 4%-6% relative to a hypothetical baseline system that wotdd produce only citation-form accentuation, giving error rate reductions of 11%-25%.</Paragraph>
  </Section>
class="xml-element"></Paper>