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Deciphering the Silent Participant: On the Use of Audio-Visual Cues for the Classification of Listener Categories in Group Discussions
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: 0000-0002-0397-6442
2015 (English)In: Proccedings of ICMI 2015, ACM Digital Library, 2015Conference paper, Published paper (Refereed)
Abstract [en]

Estimating a silent participant's degree of engagement and his role within a group discussion can be challenging, as there are no speech related cues available at the given time. Having this information available, however, can provide important insights into the dynamics of the group as a whole. In this paper, we study the classification of listeners into several categories (attentive listener, side participant and bystander). We devised a thin-sliced perception test where subjects were asked to assess listener roles and engagement levels in 15-second video-clips taken from a corpus of group interviews. Results show that humans are usually able to assess silent participant roles. Using the annotation to identify from a set of multimodal low-level features, such as past speaking activity, backchannels (both visual and verbal), as well as gaze patterns, we could identify the features which are able to distinguish between different listener categories. Moreover, the results show that many of the audio-visual effects observed on listeners in dyadic interactions, also hold for multi-party interactions. A preliminary classifier achieves an accuracy of 64 %.

Place, publisher, year, edition, pages
ACM Digital Library, 2015.
National Category
Computer Science Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:kth:diva-180426DOI: 10.1145/2818346.2820759ISI: 000380609500018Scopus ID: 2-s2.0-84959309012ISBN: 978-1-4503-3912-4 (print)OAI: oai:DiVA.org:kth-180426DiVA: diva2:893710
Conference
ICMI 2015
Note

QC 20160121

Available from: 2016-01-13 Created: 2016-01-13 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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Citation style
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