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Gaze direction as a Back-Channel inviting Cue in Dialogue
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.ORCID iD: 0000-0003-3585-8077
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.ORCID iD: 0000-0002-8273-0132
2012 (English)In: IVA 2012 workshop on Realtime Conversational Virtual Agents, Santa Cruz, CA, USA, 2012Conference paper, Published paper (Refereed)
Abstract [en]

In this study, we experimentally explore the relationship between gaze direction and backchannels in face-to-face interaction. The overall motivation is to use gaze direction in a virtual agent as a mean to elicit user feedback. The relationship between gaze and backchannels was tested in an experiment in which participants were asked to provide feedback when listening to a story-telling virtual agent. When speaking, the agent shifted her gaze towards the listener at predefined positions in the dialogue. The results show that listeners are more prone to backchannel when the virtual agent’s gaze is directed towards them than when it is directed away. However, there is a high response variability for different dialogue contexts which suggests that the timing of backchannels cannot be explained by gaze direction alone.

Place, publisher, year, edition, pages
Santa Cruz, CA, USA, 2012.
National Category
Computer Science Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:kth:diva-109388OAI: oai:DiVA.org:kth-109388DiVA: diva2:581725
Conference
IVA 2012 workshop on Realtime Conversational Virtual Agents, September 15th, 2012, Santa Cruz, California
Note

tmh_import_13_01_02, tmh_id_3785. QC 20130103

Available from: 2013-01-02 Created: 2013-01-02 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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Hjalmarsson, Anna

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