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

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?

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

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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


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