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<Paper uid="P99-1040">
  <Title>Automatic Detection of Poor Speech Recognition at the Dialogue Level</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> The dialogue strategies used by a spoken dialogue system strongly influence performance and user satisfaction. An ideal system would not use a single fixed strategy, but would adapt to the circumstances at hand. To do so, a system must be able to identify dialogue properties that suggest adaptation. This paper focuses on identifying situations where the speech recognizer is performing poorly. We adopt a machine learning approach to learn rules from a dialogue corpus for identifying these situations.</Paragraph>
    <Paragraph position="1"> Our results show a significant improvement over the baseline and illustrate that both lower-level acoustic features and higher-level dialogue features can affect the performance of the learning algorithm.</Paragraph>
  </Section>
class="xml-element"></Paper>