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<Paper uid="P01-1048">
  <Title>Predicting User Reactions to System Error</Title>
  <Section position="6" start_page="0" end_page="0" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> Continuing our earlier research into the use of prosodic information to identify system misrecognitions and user corrections in a SDS, we have studied aware sites, turns in which a user first notices a system error. We find first that these sites have prosodic properties which distinguish them from other turns, such as corrections and normal turns. Subsequent machine learning experiments distinguishing aware sites from corrections and from normal turns show that aware sites can be classified as such automatically, with a considerable degree of accuracy. In particular, in a 2-way classification of aware sites vs. all other turns we achieve an estimated success rate of 87.8%.</Paragraph>
    <Paragraph position="1"> Such classification, we believe, will be especially useful in error-handling for SDS. We have previously shown that misrecognitions can be classified with considerable accuracy, using prosodic and other automatically available features. With our new success in identifying aware sites, we acquire another potentially powerful indicator of prior error. Using these two indicators together, we hope to target system errors considerably more accurately than current SDS can do and to hypothesize likely locations of user attempts to correct these errors. Our future research will focus upon combining these sources of information identifying system errors and user corrections, and investigating strategies to make use of this information, including changes in dialogue strategy (e.g. from user or mixed initiative to system initiative after errors) and the use of specially trained acoustic models to better recognize corrections.</Paragraph>
  </Section>
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