Multimodal Corpus of Bidirectional Conversation of Human-human and Human-robot Interaction during fMRI Scanning

Birgit Rauchbauer, Youssef Hmamouche, Brigitte Bigi, Laurent Prévot, Magalie Ochs, Thierry Chaminade


Abstract
In this paper we present investigation of real-life, bi-directional conversations. We introduce the multimodal corpus derived from these natural conversations alternating between human-human and human-robot interactions. The human-robot interactions were used as a control condition for the social nature of the human-human conversations. The experimental set up consisted of conversations between the participant in a functional magnetic resonance imaging (fMRI) scanner and a human confederate or conversational robot outside the scanner room, connected via bidirectional audio and unidirectional videoconferencing (from the outside to inside the scanner). A cover story provided a framework for natural, real-life conversations about images of an advertisement campaign. During the conversations we collected a multimodal corpus for a comprehensive characterization of bi-directional conversations. In this paper we introduce this multimodal corpus which includes neural data from functional magnetic resonance imaging (fMRI), physiological data (blood flow pulse and respiration), transcribed conversational data, as well as face and eye-tracking recordings. Thus, we present a unique corpus to study human conversations including neural, physiological and behavioral data.
Anthology ID:
2020.lrec-1.84
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
COLING | LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
668–675
Language:
English
URL:
https://www.aclweb.org/anthology/2020.lrec-1.84
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.lrec-1.84.pdf