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Linking speaking and looking behavior patterns with group composition, perception, and performance

Authors:

Dineshbabu Jayagopi,

Dairazalia Sanchez-Cortes,

Kazuhiro Otsuka,

Daniel Gatica-PerezAuthors Info & Claims

ICMI '12: Proceedings of the 14th ACM international conference on Multimodal interaction

Pages 433 - 440

https://doi.org/10.1145/2388676.2388772

Published: 22 October 2012 Publication History

Abstract

This paper addresses the task of mining typical behavioral patterns from small group face-to-face interactions and linking them to social-psychological group variables. Towards this goal, we define group speaking and looking cues by aggregating automatically extracted cues at the individual and dyadic levels. Then, we define a bag of nonverbal patterns (Bag-of-NVPs) to discretize the group cues. The topics learnt using the Latent Dirichlet Allocation (LDA) topic model are then interpreted by studying the correlations with group variables such as group composition, group interpersonal perception, and group performance. Our results show that both group behavior cues and topics have significant correlations with (and predictive information for) all the above variables. For our study, we use interactions with unacquainted members i.e. newly formed groups.

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Cited By

Vuillecard PFarkhondeh AVillamizar MOdobez J(2024)CCDb-HG: Novel Annotations and Gaze-Aware Representations for Head Gesture Recognition2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG)10.1109/FG59268.2024.10581954(1-9)Online publication date: 27-May-2024
https://doi.org/10.1109/FG59268.2024.10581954
Mawalim COkada SNakano YUnoki M(2023)Personality trait estimation in group discussions using multimodal analysis and speaker embeddingJournal on Multimodal User Interfaces10.1007/s12193-023-00401-017:2(47-63)Online publication date: 8-Feb-2023
https://doi.org/10.1007/s12193-023-00401-0
Schneider BSung GChng EYang S(2021)How Can High-Frequency Sensors Capture Collaboration? A Review of the Empirical Links between Multimodal Metrics and Collaborative ConstructsSensors10.3390/s2124818521:24(8185)Online publication date: 8-Dec-2021
https://doi.org/10.3390/s21248185
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Index Terms

Linking speaking and looking behavior patterns with group composition, perception, and performance
1. Human-centered computing
  1. Collaborative and social computing

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Information & Contributors

Information

Published In

cover image ACM Conferences

ICMI '12: Proceedings of the 14th ACM international conference on Multimodal interaction

October 2012

636 pages

ISBN:9781450314671

DOI:10.1145/2388676

General Chairs:
Louis-Philippe Morency
University of Southern California, USA
,
Dan Bohus
Microsoft Research, USA
,
Hamid Aghajan
Stanford University, USA
,
Program Chairs:
Justine Cassell
Carnegie Mellon University, USA
,
Anton Nijholt
University of Twente, Netherlands
,
Julien Epps
The University of New South Wales, Australia

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 October 2012

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ICMI '12

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SIGCHI

ICMI '12: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

October 22 - 26, 2012

California, Santa Monica, USA

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Overall Acceptance Rate 453 of 1,080 submissions, 42%

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Cited By

Vuillecard PFarkhondeh AVillamizar MOdobez J(2024)CCDb-HG: Novel Annotations and Gaze-Aware Representations for Head Gesture Recognition2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG)10.1109/FG59268.2024.10581954(1-9)Online publication date: 27-May-2024
https://doi.org/10.1109/FG59268.2024.10581954
Mawalim COkada SNakano YUnoki M(2023)Personality trait estimation in group discussions using multimodal analysis and speaker embeddingJournal on Multimodal User Interfaces10.1007/s12193-023-00401-017:2(47-63)Online publication date: 8-Feb-2023
https://doi.org/10.1007/s12193-023-00401-0
Schneider BSung GChng EYang S(2021)How Can High-Frequency Sensors Capture Collaboration? A Review of the Empirical Links between Multimodal Metrics and Collaborative ConstructsSensors10.3390/s2124818521:24(8185)Online publication date: 8-Dec-2021
https://doi.org/10.3390/s21248185
Morgan MBhattacharya IRadke RBraasch J(2021)Classifying the emotional speech content of participants in group meetings using convolutional long short-term memory networkThe Journal of the Acoustical Society of America10.1121/10.0003433149:2(885-894)Online publication date: Feb-2021
https://doi.org/10.1121/10.0003433
Yagi YOkada SShiobara SSugimura S(2021)Predicting multimodal presentation skills based on instance weighting domain adaptationJournal on Multimodal User Interfaces10.1007/s12193-021-00367-x16:1(1-16)Online publication date: 18-Feb-2021
https://doi.org/10.1007/s12193-021-00367-x
Becattini NAlberio L(2021)Semi-automatic Analysis of Spoken Interaction Dynamics in Collaborative Design SessionsDesign Tools and Methods in Industrial Engineering II10.1007/978-3-030-91234-5_19(183-195)Online publication date: 1-Dec-2021
https://doi.org/10.1007/978-3-030-91234-5_19
Otsuka KTsumori M(2020)Analyzing Multifunctionality of Head Movements in Face-to-Face Conversations Using Deep Convolutional Neural NetworksIEEE Access10.1109/ACCESS.2020.30416728(217169-217195)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3041672
Nihei FNakano Y(2019)Exploring Methods for Predicting Important Utterances Contributing to Meeting SummarizationMultimodal Technologies and Interaction10.3390/mti30300503:3(50)Online publication date: 6-Jul-2019
https://doi.org/10.3390/mti3030050
Li SOkada SDang J(2019)Interaction Process Label Recognition in Group Discussion2019 International Conference on Multimodal Interaction10.1145/3340555.3353743(426-434)Online publication date: 14-Oct-2019
https://dl.acm.org/doi/10.1145/3340555.3353743
Bhattacharya IFoley MKu CZhang NZhang TMine CLi MJi HRiedl CWelles BRadke RZink MToni LBegen A(2019)The unobtrusive group interaction (UGI) corpusProceedings of the 10th ACM Multimedia Systems Conference10.1145/3304109.3325816(249-254)Online publication date: 18-Jun-2019
https://dl.acm.org/doi/10.1145/3304109.3325816
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