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<Paper uid="W04-2305">
  <Title>Combining Acoustic Confidences and Pragmatic Plausibility for Classifying Spoken Chess Move Instructions</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper describes a machine learning approach to classifying n-best speech recognition hypotheses as either correctly or incorrectly recognised. The learners are trained on a combination of acoustic confidence features and move evaluation scores in a chess-playing scenario. The results show significant improvements over sharp baselines that use confidence rejection thresholds for classification.</Paragraph>
  </Section>
class="xml-element"></Paper>