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The Tutorbot Corpus - A Corpus for Studying Tutoring Behaviour in Multiparty Face-to-Face Spoken Dialogue
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH.
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2014 (English)Conference paper (Refereed)
Abstract [en]

This paper describes a novel experimental setup exploiting state-of-the-art capture equipment to collect a multimodally rich game-solving collaborative multiparty dialogue corpus. The corpus is targeted and designed towards the development of a dialogue system platform to explore verbal and nonverbal tutoring strategies in multiparty spoken interactions. The dialogue task is centered on two participants involved in a dialogue aiming to solve a card-ordering game. The participants were paired into teams based on their degree of extraversion as resulted from a personality test. With the participants sits a tutor that helps them perform the task, organizes and balances their interaction and whose behavior was assessed by the participants after each interaction. Different multimodal signals captured and auto-synchronized by different audio-visual capture technologies, together with manual annotations of the tutor’s behavior constitute the Tutorbot corpus. This corpus is exploited to build a situated model of the interaction based on the participants’ temporally-changing state of attention, their conversational engagement and verbal dominance, and their correlation with the verbal and visual feedback and conversation regulatory actions generated by the tutor.

Place, publisher, year, edition, pages
Keyword [en]
Multimodal corpus; Multiparty Interaction; Tutor
National Category
Computer Science
URN: urn:nbn:se:kth:diva-173469ISI: 000355611005138OAI: diva2:853919
9th International Conference on Language Resources and Evaluation, Reykjavik, Iceland

QC 20161017

Available from: 2015-09-15 Created: 2015-09-11 Last updated: 2016-10-17Bibliographically approved

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Al Moubayed, SamerBollepalli, BajibabuJohansson, MartinOertel, CatharineStefanov, Kalin
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Speech, Music and Hearing, TMH
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