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<Paper uid="P04-1044">
  <Title>Combining Acoustic and Pragmatic Features to Predict Recognition Performance in Spoken Dialogue Systems</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We use machine learners trained on a combination of acoustic confidence and pragmatic plausibility features computed from dialogue context to predict the accuracy of incoming n-best recognition hypotheses to a spoken dialogue system. Our best results show a 25% weighted f-score improvement over a baseline system that implements a &amp;quot;grammar-switching&amp;quot; approach to context-sensitive speech recognition.</Paragraph>
  </Section>
class="xml-element"></Paper>