7. Gender effect on Humor-Prosody (Results) Accounting for gender differences with 2-way ANOVA The test shows –

1. Humor: Prosody Analysis and Automatic Recognition For F.R.I.E.N.D.S Amruta Purandare and Diane Litman University of Pittsburgh EMNLP 2006, Sydney, Australia • 1. Motivation • Need social intelligence in computers • Approaches: Affect, Personality, Humor? • State of the art in Computational Humor: • Humorous Text (Acronyms, One-liners, Wordplays) • Lexical cues (Alliteration, Slang, Antonymy) • Our contribution: • Humor Detection in Spoken Conversations • Do Prosodic cues (e.g. Pitch, Intensity, Tempo) help? • 6. Humor-Prosody Analysis (Results) • Most prosodic features show significant (p<=0.05 for a t-test) • differences between Humor and Non-Humor groups • Humorous turns show higher Max, Range, Std-Dev in Pitch and • Energy, higher Tempo and smaller Internal Silence • 7. Gender effect on Humor-Prosody (Results) • Accounting for gender differences with 2-way ANOVA • The test shows – • Humor effect on prosody adjusted for Gender • Gender effect on prosody adjusted for Humor • Interaction effect between Gender and Humor • i.e. if the prosodic style of expressing humor is different • for Males and Females • Findings: • Significant effect of • Humor even when • adjusted for Gender • 2) Significant effect of • Gender, but only Pitch • features show the Interaction • Effect. i.e. males and females • use different Pitch variations • while expressing Humor • 2. FRIENDS Corpus • 75 Dialogs from a classic TV-comedy: FRIENDS • 2hrs of Audio • Text transcripts from: http://www.friendscafe.org/scripts • Humorous turns are followed by laughs • Automatic labeling using laughs • Corpus size = 1629 turns • 714 Humorous, 915 Non-Humorous • 6 Main Actors (3 Male, 3 Female), 26 Guest Actors Y: significant effect N: non-significant effect 3. Example Dialog Rachel: Guess what? [no] Ross: You got a job? [no] Rachel: Are you kidding? I am trained for nothing! [yes] <laugh> Rachel: I was laughed out of 12 interviews today. [no] Chandler: and yet you are surprisingly upbeat! [no] Rachel: Well, you would be too, if you found John & David’s boots on sale, 50% off... [yes] <laugh> Chandler: Oh how well you know me! [yes] <laugh> Rachel: They are my new, I don’t need a job, I don’t need my parents, I got great Boots, Boots! [yes] <laugh> [yes] Humorous Turns [no] Non-Humorous Turns • 8. Humor Recognition (Results) • Supervised 2-way classification • Results above baseline (56.2%) • Results consistent for genders • Marginal improvement higher for males • Decision tree shows that the algorithm picked mostly • prosodic and speaker features in the first 10 iterations • 4. Features • Borrowed from emotional speech literature • Prosodic (13) • Pitch (F0): Mean, Max, Range, Std-Dev • Energy (RMS): Mean, Max, Range, Std-Dev • Temporal: Duration, Internal Silence, Tempo • Lexical (2011) • all Words • Turn Length (#words in the turn) • Speaker ID (1) • 9. Conclusions & Future Work • Humor recognition in spoken conversations • Data • Dialogs from a classic comedy TV show, FRIENDS • Used laughs for automatically labeling humorous turns • Humor-Prosody Analysis • Humorous turns show higher peaks and variations in pitch and • energy, and higher tempo, compared to non-humorous turns • Gender Effect • Most features show humor effect even when adjusted for gender • Only pitch features show the interaction effect • Results • Promising, 8% over the baseline with all features • Humor detection easier for male speakers than for females • Future • Pragmatic features • e.g. Ambiguity, Incongruity, Expectation-Violation etc. 5. Feature Extraction using Wavesurfer