Gaze and turn-taking behavior in casual conversational interactions
Abstract
Eye gaze is an important means for controlling interaction and coordinating the participants' turns smoothly. We have studied how eye gaze correlates with spoken interaction and especially focused on the combined effect of the speech signal and gazing to predict turn taking possibilities. It is well known that mutual gaze is important in the coordination of turn taking in two-party dialogs, and in this article, we investigate whether this fact also holds for three-party conversations. In group interactions, it may be that different features are used for managing turn taking than in two-party dialogs. We collected casual conversational data and used an eye tracker to systematically observe a participant's gaze in the interactions. By studying the combined effect of speech and gaze on turn taking, we aimed to answer our main questions: How well can eye gaze help in predicting turn taking? What is the role of eye gaze when the speaker holds the turn? Is the role of eye gaze as important in three-party dialogs as in two-party dialogue? We used Support Vector Machines (SVMs) to classify turn taking events with respect to speech and gaze features, so as to estimate how well the features signal a change of the speaker or a continuation of the same speaker. The results confirm the earlier hypothesis that eye gaze significantly helps in predicting the partner's turn taking activity, and we also get supporting evidence for our hypothesis that the speaker is a prominent coordinator of the interaction space. Such a turn taking model could be used in interactive applications to improve the system's conversational performance.
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Gaze can serve as a turn-taking cue, a backchannel-inviting cue from the speaker toward the listener, and/or an indication of mutual attention.
“Gaze can serve as a turn-taking cue (Novick et al., 1996; Jokinen et al., 2013), a backchannel-inviting cue from the speaker toward the listener (Bavelas et al., 2002; Hjalmarsson and Oertel, 2012), and/or an indication of mutual attention (Frischen et al., 2007).”
From Modeling Feedback in Interaction With Conversational Agents—A Review (2022) · cites this paper as Jokinen et al., 2013
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Self-citationConnecting the Brains via Virtual Eyes : Eye-Gaze Directions and Inter-brain Synchrony in VR
Gumilar, I., Barde, A., Hayati, A. F., Billinghurst, M., Lee, G., Momin, A., Averill, C., & Dey, A. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3451583 · Cited by 8 · see citing works
Trimodal prediction of speaking and listening willingness to help improve turn-changing modeling
Ishii, R., Ren, X., Muszyński, M., & Morency, L. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.774547 · Cited by 8 · see citing works
SealMates: Improving Communication in Video Conferencing using a Collective Behavior-Driven Avatar
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"Put Your Hands Up": How Joint Attention Is Initiated Between Blind Children And Their Sighted Peers
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On the possibility of predicting gaze aversion to improve video-chat efficiency
Ward, N., Jurado, C. N., Garcia, R. A., & Ramos, F. A. (2016) · ACM Symposium on Eye Tracking Research and Applications (ETRA) · doi:10.1145/2857491.2857497 · Cited by 6 · see citing works
Multimodal Analysis of Cohesion in Multi-party InteractionsPreprint
Kantharaju, R. B., Langlet, C., Barange, M., Clavel, C., & Pélachaud, C. (2020) · Cited by 6 · see citing works
Autonomous AI Agents for Multi-Platform Social Media Marketing: A Simultaneous Deployment Study
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Unraveling the thread: understanding and addressing sequential failures in human-robot interaction
Tisserand, L., Stephenson, B., Baldauf-Quilliatre, H., Lefort, M., & Armetta, F. (2024) · Frontiers in Robotics and AI · doi:10.3389/frobt.2024.1359782 · Cited by 7 · see citing works
Next-Speaker Prediction Based on Non-Verbal Information in Multi-Party Video Conversation
Mizuno, S., Hojo, N., Kobashikawa, S., & Masumura, R. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094679 · Cited by 5 · see citing works
Using Transition Duration to Improve Turn-taking in Conversational Agents
Threlkeld, C., Umair, M., & Ruiter, J. P. D. (2022) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2022.sigdial-1.20 · Cited by 5 · see citing works
To laugh or not to laugh? The use of laughter to mark discourse structure
Ludusan, B., & Schuppler, B. (2022) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2022.sigdial-1.8 · Cited by 5 · see citing works
Quantifying Social Connection With Verbal and Non-Verbal Behaviors in Virtual Reality Conversations
Kim, H., & Lee, J. (2025) · International Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713674 · Cited by 6 · see citing works
Mobile eye-tracking and mixed-methods approaches to interaction analysis
Oben, B., de Vries, C., & Brône, G. (2025) · Pragmatics & beyond. New series · doi:10.1075/pbns.351.04obe · Cited by 4 · see citing works
Modeling Japanese Praising Behavior by Analyzing Audio and Visual Behaviors
Onishi, T., Yamauchi, A., Ogushi, A., Ishii, R., Fukayama, A., Nakamura, T., & Miyata, A. (2022) · Frontiers in Computer Science · doi:10.3389/fcomp.2022.815128 · Cited by 4 · see citing works
Examining the Impact of Digital Human Gaze Expressions on Engagement Induction
Mok, S., Park, S., & Whang, M. (2023) · Biomimetics · doi:10.3390/biomimetics8080610 · Cited by 4 · see citing works
A Computational Study on Sentence-based Next Speaker Prediction in Multiparty Conversations
Lee, M.-C., Li, W. A., & Deng, Z. (2024) · ACM International Conference on Intelligent Virtual Agents (IVA) · doi:10.1145/3652988.3673915 · Cited by 4 · see citing works
Tracking Discourse Topics in Co-speech Gesture
Laparle, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77817-0_18 · Cited by 3 · see citing works
Human Event Recognition in Smart Classrooms Using Computer Vision: A Systematic Literature Review
Córdoba-Tlaxcalteco, M., & Benítez-Guerrero, E. (2023) · Programming and computer software · doi:10.1134/s0361768823080066 · Cited by 3 · see citing works
A Robot Commenting Texts in an Emotional Way
Volkova, L., Kotov, A. A., Klyshinsky, E. S., & Arinkin, N. A. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-65551-2_19 · Cited by 3 · see citing works
Turn-taking intention recognition using multimodal cues in social human-robot interaction
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Data-Driven Control of Eye and Head Movements for Triadic Human-Robot Interactions
Haefflinger, L., Elisei, F., & Bailly, G. (2025) · International Journal of Social Robotics · doi:10.1007/s12369-025-01245-2 · Cited by 2 · see citing works
Gaze behaviour and vocal feedback in task-based dyadic conversations with and without eye contact
Sbranna, S., Savino, M., Baills, F., & Grice, M. (2025) · Frontiers in Communication · doi:10.3389/fcomm.2025.1524453 · Cited by 2 · see citing works
TMAN: A temporal multimodal attention network for backchannel detection
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Gaze from and Toward the Silent Third Participant in a Triadic Conversation
Umata, I., Ijuin, K., Kato, T., & Yamamoto, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21905-5_6 · Cited by 2 · see citing works
Self-citationG-DAIC: A Gaze Initialized Framework for Description and Aesthetic-Based Image Cropping
Horányi, N., Hou, Y., Leonardis, A., & Chang, H. J. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3591132 · Cited by 2 · see citing works
Conversación y mirada en aulas urbanas y rurales de primer grado
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Ijuin, K., & Jokinen, K. (2019) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3349537.3352792 · Cited by 2 · see citing works
Self-citationAnalysis of Timing Structure of Eye Contact in Turn-changing
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Eye Gaze Analyses in L1 and L2 Conversations
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Floor Apportionment Function of Speaker's Gaze in Grounding Acts
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Self-citationAn Annotation Approach for Social and Referential Gaze in Dialogue.
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Self-citationEye Gaze Analyses in L1 and L2 Conversations: Difference in Interaction Structures
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Effects of Linguistic Proficiency and Conversation Topic on Listener’s Gaze in Triadic Conversation
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Self-citationInteractive Creativity Activities in Remote Learning
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Towards multimodal expression of information reliability in HRI
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- Authors
- Jokinen, K., Furukawa, H., Nishida, M., & Yamamoto, S.
- Affiliations
- University of Helsinki (FI)
- Doshisha University (JP)
- Venue
- ACM Transactions on Interactive Intelligent Systems
- Published
- 2013-07
- Volume
- 3
- Issue
- 2
- Pages
- 1-30
- ISSN
- 2160-6455 (ISSN-L)
- 2160-6463
- OpenAlex
- W2141666434
- Cited by
- 135
- References
- 72
- Citations by year
- 2026: 11 · 2025: 19 · 2024: 12 · 2023: 8 · 2022: 31 · 2021: 12 · 2020: 7 · 2019: 8 · 2018: 5 · 2017: 4 · 2016: 6 · 2015: 6 · 2014: 4 · 2013: 1 · 2012: 1
- FWCI
- 6.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Interactive Intelligent Systems (publishedVersion)
- Funders
- Japan Society for the Promotion of Science (25330210)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Social Sciences › Arts and Humanities › Language and Linguistics › Language, Discourse, Communication Strategies
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- gaze, turn-taking, everyday conversation, speaker change detection, group interaction, chatbots
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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