Laughter in task-based settings: Whom we talk to affects how, when, and how often we laugh

Catarina Branco, Isabel Trancoso, Paulo Infante, Khiet P. Truong

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

55 Downloads (Pure)

Abstract

Map task corpora are not typically used to study laughter, but they allow an interesting analysis of multiple factors such as familiarity between the participants, their gender, and eye contact. We conducted linear/generalized mixed-effects analysis to study if co-laughter, laughter rate, and the percentage of voiced frames in laughs are influenced by such factors. Our results show that, in conversations without eye contact, the gender of the participant was statistically relevant regarding laughter rate and the percentage of voiced frames, and the difference in gender was relevant regarding co-laughter. On the other hand, with eye contact, familiarity was statistically relevant with respect to co-laughter, laughter rate, and the percentage of voiced frames. Most of our results align and extend what has been previously found, except for voiced laughs between friends. This study emphasizes the highly variable character of laughter and its dependence on interlocutors' characteristics.
Original languageEnglish
Title of host publicationProceedings of INTERSPEECH 2023
EditorsNaomi Harte, Julie Carson-Berndsen, Gareth Jones
Place of PublicationBaixas, France
PublisherInternational Speech Communication Association
Pages3622-3626
DOIs
Publication statusPublished - 2023
Event24th INTERSPEECH 2023 - Dublin, Ireland
Duration: 20 Aug 202324 Aug 2023
Conference number: 24
https://www.interspeech2023.org/

Conference

Conference24th INTERSPEECH 2023
Country/TerritoryIreland
CityDublin
Period20/08/2324/08/23
Internet address

Fingerprint

Dive into the research topics of 'Laughter in task-based settings: Whom we talk to affects how, when, and how often we laugh'. Together they form a unique fingerprint.

Cite this