Perceived Conversation Quality in Spontaneous Interactions

Journal Article (2023)
Authors

C.A. Raman (TU Delft - Pattern Recognition and Bioinformatics)

Navin Raj Prabhu (Student TU Delft)

Hayley Hung (TU Delft - Pattern Recognition and Bioinformatics)

Research Group
Pattern Recognition and Bioinformatics
Copyright
© 2023 C.A. Raman, Navin Raj Prabhu, H.S. Hung
To reference this document use:
https://doi.org/10.1109/TAFFC.2023.3233950
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 C.A. Raman, Navin Raj Prabhu, H.S. Hung
Research Group
Pattern Recognition and Bioinformatics
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. @en
Issue number
4
Volume number
14
Pages (from-to)
2901-2912
DOI:
https://doi.org/10.1109/TAFFC.2023.3233950
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Abstract

The quality of daily spontaneous conversations is of importance towards both our well-being as well as the development of interactive social agents. Prior research directly studying the quality of social conversations has operationalized it in narrow terms, associating greater quality to less small talk. Other works taking a broader perspective of interaction experience have indirectly studied quality through one of the several overlapping constructs such as rapport or engagement, in isolation. In this work we bridge this gap by proposing a holistic conceptualization of conversation quality, building upon the collaborative attributes of cooperative conversation floors. Taking a multilevel perspective of conversation, we develop and validate two instruments for perceived conversation quality (PCQ) at the individual and group levels. Specifically, we motivate capturing external raters' gestalt impressions of participant experiences from thin slices of behavior, and collect annotations of PCQ on the publicly available MatchNMingle dataset of in-the-wild mingling conversations. Finally, we present an analysis of behavioral features that are predictive of PCQ. We find that for the conversations in MatchNMingle, raters tend to associate smaller group sizes, equitable speaking turns with fewer interruptions, and time taken for synchronous bodily coordination with higher PCQ.

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