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<Paper uid="W06-1625">
  <Title>Humor: Prosody Analysis and Automatic Recognition for F * R * I * E * N * D * S *</Title>
  <Section position="12" start_page="213" end_page="214" type="concl">
    <SectionTitle>
10 Conclusions
</SectionTitle>
    <Paragraph position="0"> In this paper, we presented our experiments on humor-prosody analysis and humor recognition in spoken conversations, collected from a classic television comedy, FRIENDS. Using a simple automated annotation scheme, we labeled speaker turns in our corpus that are followed by artificial laughs as humorous, and the rest as non-humorous. We then examined a number of acoustic-prosodic features based on pitch, energy and temporal information in the speech signal, that have been found useful by previous studies in emotion recognition.</Paragraph>
    <Paragraph position="1"> Our prosody analysis revealed that humorous and non-humorous turns indeed show significant differences in most of these features, even when accounted for the speaker and gender differences. Specifically, we found that humorous turns tend to have higher tempo, smaller internal silence, and higher peak, range and standard deviation for pitch and energy, compared to non-humorous turns.</Paragraph>
    <Paragraph position="2"> On the humor recognition task, our classifier</Paragraph>
    <Section position="1" start_page="214" end_page="214" type="sub_section">
      <SectionTitle>
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      <Paragraph position="0"> achieved the best performance when acoustic-prosodic features were used in conjunction with lexical and other types of features, and in all experiments attained the accuracy statistically significant over the baseline. While prosody of humor shows some differences due to gender, the performance on the humor recognition task is equivalent for males and females, although the relative improvement over the baseline is much higher for males than for females.</Paragraph>
      <Paragraph position="1"> Our current study focuses only on lexical and speech features, primarily because these features can be computed automatically. In the future, we plan to explore more sophisticated semantic and pragmatic features such as incongruity, ambiguity, expectation-violation etc. We also like to investigate if our findings generalize to other types of corpora besides TV-show dialogs.</Paragraph>
    </Section>
  </Section>
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