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

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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