Papers by Laurent Prévot
Multimodal Corpus of Bidirectional Conversation of Human-human and Human-robot Interaction during fMRI Scanning (2020.lrec-1)
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Birgit Rauchbauer, Youssef Hmamouche, Brigitte Bigi, Laurent Prévot, Magalie Ochs, Thierry Chaminade
| Challenge: | a study of real-life bi-directional conversations combines multimodal corpus with neural, physiological and behavioral data. |
| Approach: | They propose a multimodal corpus derived from natural conversations . they used human-human interactions as a control condition . |
| Outcome: | The proposed corpus includes neural, physiological and behavioral data. |
The ISO Standard for Dialogue Act Annotation, Second Edition (2020.lrec-1)
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Harry Bunt, Volha Petukhova, Emer Gilmartin, Catherine Pelachaud, Alex Fang, Simon Keizer, Laurent Prévot
| Challenge: | ISO standard 24617-2 for dialogue act annotation has been used in corpus annotation and in the design of components for spoken and multimodal interactive systems. |
| Approach: | ISO standard 24617-2 for dialogue act annotation is proposed for a second edition . this second edition allows a more accurate annotation of dependence relations and rhetorical relations in dialogue. |
| Outcome: | The proposed second edition of ISO 24617-2 for dialogue act annotation addresses some inaccuracies and undesirable limitations. |
CHICA: A Developmental Corpus of Child-Caregiver’s Face-to-face vs. Video Call Conversations in Middle Childhood (2024.lrec-main)
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Dhia Elhak Goumri, Abhishek Agrawal, Mitja Nikolaus, Hong Duc Thang Vu, Kübra Bodur, Elias Emmar, Cassandre Armand, Chiara Mazzocconi, Shreejata Gupta, Laurent Prévot, Benoit Favre, Leonor Becerra-Bonache, Abdellah Fourtassi
| Challenge: | Existing studies of language-in-interaction focus on the two ends of the developmental spectrum, i.e., early childhood and adulthood, leaving a gap in our knowledge about how development unfolds, especially across middle childhood. |
| Approach: | They propose to use CHICA to analyze child-caregiver conversations at home . they use mobile, lightweight eye-tracking and head motion detection to optimize the naturalness of the recordings. |
| Outcome: | The proposed corpus of child-caregiver conversations at home was compared with a previous corpus based on a set of conversations between children aged 7, 9, and 11 years old. |
BrainPredict: a Tool for Predicting and Visualising Local Brain Activity (2020.lrec-1)
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| Challenge: | Using fMRI, we recorded a corpus of human-human and human-robot conversations while participants brain activity was recorded with f.MRI, but we did not find any tools for displaying together brain activity prediction of non-controlled conversations, the raw material used in this prediction and the features used for these predictions. |
| Approach: | They propose a tool that allows dynamic prediction and visualization of an individual’s local brain activity during a conversation using raw behavioral data. |
| Outcome: | The proposed tool takes as input behavioral features computed from raw data, mainly the participant and the interlocutor speech but also the participant’s visual input and eye movements. |