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A Gaze-based Method for Relating Group Involvement to Individual Engagement in Multimodal Multiparty Dialogue
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.ORCID iD: 0000-0002-8273-0132
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.ORCID iD: 0000-0002-3323-5311
2013 (English)In: ICMI 2013 - Proceedings of the 2013 ACM International Conference on Multimodal Interaction, Association for Computing Machinery (ACM), 2013, 99-106 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper is concerned with modelling individual engagement and group involvement as well as their relationship in an eight-party, mutimodal corpus. We propose a number of features (presence, entropy, symmetry and maxgaze) that summarise different aspects of eye-gaze patterns and allow us to describe individual as well as group behaviour in time. We use these features to define similarities between the subjects and we compare this information with the engagement rankings the subjects expressed at the end of each interactions about themselves and the other participants. We analyse how these features relate to four classes of group involvement and we build a classifier that is able to distinguish between those classes with 71% of accuracy.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2013. 99-106 p.
Keyword [en]
interaction, gaze, engagement, involvement, classication
National Category
Computer Science Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:kth:diva-137417DOI: 10.1145/2522848.2522865Scopus ID: 2-s2.0-84892620374ISBN: 978-1-4503-2129-7 (print)OAI: oai:DiVA.org:kth-137417DiVA: diva2:678863
Conference
2013 15th ACM International Conference on Multimodal Interaction, ICMI 2013; Sydney, NSW; Australia; 9 December 2013 through 13 December 2013
Note

QC 20130128

Available from: 2013-12-13 Created: 2013-12-13 Last updated: 2016-12-13Bibliographically approved
In thesis
1. Modelling Engagement in Multi-Party Conversations: Data-Driven Approaches to Understanding Human-Human Communication Patterns for Use in Human-Robot Interactions
Open this publication in new window or tab >>Modelling Engagement in Multi-Party Conversations: Data-Driven Approaches to Understanding Human-Human Communication Patterns for Use in Human-Robot Interactions
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The aim of this thesis is to study human-human interaction in order to provide virtual agents and robots with the capability to engage into multi-party-conversations in a human-like-manner. The focus lies with the modelling of conversational dynamics and the appropriate realization of multi-modal feedback behaviour. For such an undertaking, it is important to understand how human-human communication unfolds in varying contexts and constellations over time. To this end, multi-modal human-human corpora are designed as well as annotation schemes to capture conversational dynamics are developed. Multi-modal analysis is carried out and models are built. Emphasis is put on not modelling speaker behaviour in general and on modelling listener behaviour in particular.

In this thesis, a bridge is built between multi-modal modelling of conversational dynamics on the one hand multi-modal generation of listener behaviour in virtual agents and robots on the other hand. In order to build this bridge, a unit-selection multi-modal synthesis is carried out as well as a statistical speech synthesis of feedback. The effect of a variation in prosody of feedback token on the perception of third-party observers is evaluated. Finally, the effect of a controlled variation of eye-gaze is evaluated, as is the perception of user feedback in human-robot interaction.​

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2016. 87 p.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 2017:05
National Category
Engineering and Technology
Research subject
Human-computer Interaction
Identifiers
urn:nbn:se:kth:diva-198175 (URN)978-91-7729-237-1 (ISBN)
Public defence
2017-01-20, F3, Lindstedtsvägen 26, Kungl Tekniska högskolan, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20161214

Available from: 2016-12-14 Created: 2016-12-13 Last updated: 2016-12-14Bibliographically approved

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Salvi, Giampiero

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