Conference article

The Acorformed Coprus: Investigating Multimodality in Human-Human and Human-Virtual Patient Interactions

Magalie Ochs
LIS UMR 7020, Aix Marseille Université, Universit´e de Toulon, CNRS, France

Philippe Blache
LPL UMR 7309, Aix Marseille Université, Universit´e de Toulon, CNRS, France

Grégoire Montcheuil
LPL UMR 7309, Boréal Innovation, Aix Marseille Université, Universit´e de Toulon, CNRS, France

Jean-Marie Pergandi
ISM UMR 7287, Aix Marseille Université, Universit´e de Toulon, CNRS, France

Roxane Bertrand
LPL UMR 7309, Aix Marseille Universit´e, Université de Toulon, CNRS, France

Jorane Saubesty
LPL UMR 7309, Aix Marseille Universit´e, Université de Toulon, CNRS, France

Daniel Francon
Institut Paoli-Calmettes (IPC), Marseille, France

Daniel Mestre
ISM UMR 7287, Aix Marseille Universit´e, Universit´e de Toulon, CNRS, France

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Published in: Selected papers from the CLARIN Annual Conference 2018, Pisa, 8-10 October 2018

Linköping Electronic Conference Proceedings 159:12, p. 113-120

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Published: 2019-05-28

ISBN: 978-91-7685-034-3

ISSN: 1650-3686 (print), 1650-3740 (online)

Abstract

The paper aims at presenting the Acorformed corpus composed of human-human and human-machine interactions in French in the specific context of training doctors to break bad news to patients. In the context of human-human interaction, an audiovisual corpus of interactions between doctors and actors playing the role of patients during real training sessions in French medical institutions have been collected and annotated. This corpus has been exploited to develop a platform to train doctors to break bad news with a virtual patient. The platform has been exploited to collect a corpus of human-virtual patient interactions annotated semi-automatically and collected in different virtual reality environments with different degree of immersion (PC, virtual reality headset and virtual reality room).

Keywords

Multimodal corpora, Multimodal annotation, Virtual reality, Embodied Conversational Agents, Doctor-patient interaction

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