Estimating conversational dominance in multiparty interaction

Authors:
Yukiko Nakano;Yuki Fukuhara
Affiliations:
Seikei University, Tokyo, Japan;Seikei University, Tokyo, Japan
Venue:
Proceedings of the 14th ACM international conference on Multimodal interaction
Year:
2012

Citing 7
Cited 2

Social interaction: multimodal conversation with social agents

AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
The GAZE groupware system: mediating joint attention in multiparty communication and collaboration

Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Detection and application of influence rankings in small group meetings

Proceedings of the 8th international conference on Multimodal interfaces
Attentive Presentation Agents

IVA '07 Proceedings of the 7th international conference on Intelligent Virtual Agents
Multi-party, Multi-issue, Multi-strategy Negotiation for Multi-modal Virtual Agents

IVA '08 Proceedings of the 8th international conference on Intelligent Virtual Agents
How multiple concurrent users react to a quiz agent attentive to the dynamics of their game participation

Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Dominance detection in meetings using easily obtainable features

MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction

Managing chaos: models of turn-taking in character-multichild interactions

Proceedings of the 15th ACM on International conference on multimodal interaction
A dominance estimation mechanism using eye-gaze and turn-taking information

Proceedings of the 6th workshop on Eye gaze in intelligent human machine interaction: gaze in multimodal interaction

Quantified Score

Hi-index	0.00

Visualization

Abstract

It is important for conversational agents that manage multiparty conversations to recognize the group dynamics existing among the users. This paper proposes a method for estimating the conversational dominance of participants in group interactions. First, we conducted a Wizard-of-Oz experiment to collect conversational speech, and motion data. Then, we analyzed various paralinguistic speech and gaze behaviors to elucidate the factors that predict conversational dominance. Finally, by exploiting the speech and gaze data as estimation parameters, we created a regression model to estimate conversational dominance, and the multiple correlation coefficient of this model was 0.85.