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<Paper uid="N04-1026">
  <Title>Predicting Emotion in Spoken Dialogue from Multiple Knowledge Sources</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We examine the utility of multiple types of turn-level and contextual linguistic features for automatically predicting student emotions in human-human spoken tutoring dialogues. We rst annotate student turns in our corpus for negative, neutral and positive emotions. We then automatically extract features representing acoustic-prosodic and other linguistic information from the speech signal and associated transcriptions. We compare the results of machine learning experiments using different feature sets to predict the annotated emotions.</Paragraph>
    <Paragraph position="1"> Our best performing feature set contains both acoustic-prosodic and other types of linguistic features, extracted from both the current turn and a context of previous student turns, and yields a prediction accuracy of 84.75%, which is a 44% relative improvement in error reduction over a baseline. Our results suggest that the intelligent tutoring spoken dialogue system we are developing can be enhanced to automatically predict and adapt to student emotions.</Paragraph>
  </Section>
class="xml-element"></Paper>