We analyze humorous spoken conversations from a classic comedy television show, FRIENDS, by examining acousticprosodic and linguistic features and their utility in automatic humor recognition. Using a simple annotation scheme, we automatically label speaker turns in our corpus that are followed by laughs as humorous and the rest as non-humorous. Our humor-prosody analysis reveals significant differences in prosodic characteristics (such as pitch, tempo, energy etc.) of humorous and non-humorous speech, even when accounted for the gender and speaker differences. Humor recognition was carried out using standard supervised learning classifiers, and shows promising results significantly above the baseline.
They used standard supervised learning classifiers to identify humorous speech REF ).
Humor: Prosody Analysis And Automatic Recognition For F*R*I*E*N*D*S*
Conference On Empirical Methods In Natural Language Processing
Computer Science