Humor: Prosody Analysis and Automatic Recognition for F*R*I*E*N*D*S*

Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), pages 208–215, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Humor: Prosody Analysis and Automatic Recognition for F * R * I * E * N * D * S * Amruta Purandare and Diane Litman Intelligent Systems Program University of Pittsburgh famruta,litman g@cs.pitt.edu Abstract We analyze humorous spoken conversa- tions from a classic comedy television show, FRIENDS, by examining acoustic- prosodic and linguistic features and their utility in automatic humor recognition. Using a simple annotation scheme, we au- tomatically label speaker turns in our cor- pus that are followed by laughs as hu- morous and the rest as non-humorous. Our humor-prosody analysis reveals sig- nificant differences in prosodic character- istics (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. 1 Introduction As conversational systems are becoming preva- lent in our lives, we notice an increasing need for adding social intelligence in computers. There has been a considerable amount of research on incor- porating affect (Litman and Forbes-Riley, 2004) (Alm et al., 2005) (D’Mello et al., 2005) (Shroder and Cowie, 2005) (Klein et al., 2002) and person- ality (Gebhard et al., 2004) in computer interfaces, so that, for instance, user frustrations can be rec- ognized and addressed in a graceful manner. As (Binsted, 1995) correctly pointed out, one way to alleviate user frustrations, and to make human- computer interaction more natural, personal and interesting for the users, is to model HUMOR. Research in computational humor is still in very early stages, partially because humorous lan- guage often uses complex, ambiguous and incon- gruous syntactic and semantic expressions (At- tardo, 1994) (Mulder and Nijholt, 2002) which re- quire deep semantic interpretation. Nonetheless, recent studies have shown a feasibility of auto- matically recognizing (Mihalcea and Strapparava, 2005) (Taylor and Mazlack, 2004) and generating (Binsted and Ritchie, 1997) (Stock and Strappar- ava, 2005) humor in computer systems. The state of the art research in computational humor (Bin- sted et al., 2006) is, however, limited to text (such as humorous one-liners, acronyms or wordplays), and to our knowledge, there has been no work to date on automatic humor recognition in spoken conversations. Before we can model humor in real application systems, we must first analyze features that char- acterize humor. Computational approaches to hu- mor recognition so far primarily rely on lexical and stylistic cues such as alliteration, antonyms, adult slang (Mihalcea and Strapparava, 2005). The focus of our study is, on the other hand, on ana- lyzing acoustic-prosodic cues (such as pitch, in- tensity, tempo etc.) in humorous conversations and testing if these cues can help us to auto- matically distinguish between humorous and non- humorous (normal) utterances in speech. We hy- pothesize that not only the lexical content but also the prosody (or how the content is expressed) makes …

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