GALLAND, Lucie; PELACHAUD, Catherine; PECUNE, Florian

doi:10.1109/fg59268.2024.10581979

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GALLAND, Lucie
Institut des Systèmes Intelligents et de Robotique [ISIR]

PELACHAUD, Catherine
Institut des Systèmes Intelligents et de Robotique [ISIR]

PECUNE, Florian
Sommeil, Addiction et Neuropsychiatrie [Bordeaux] [SANPSY]

Langue

Communication dans un congrès

Ce document a été publié dans

2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG), 2024-05-28, Istanbul. p. 1-9

IEEE

Résumé en anglais

Motivational Interviewing (MI) is an approach to therapy that emphasizes collaboration and encourages behavioral change. To evaluate the quality of an MI conversation, client utterances can be classified using the MISC code as either change talk, sustain talk, or follow/neutral talk. The proportion of change talk in a MI conversation is positively correlated with therapy outcomes, making accurate classification of client utterances essential. In this paper, we present a classifier that accurately distinguishes between the three MISC classes (change talk, sustain talk, and follow/neutral talk) leveraging multimodal features such as text, prosody, facial expressivity, and body expressivity. To train our model, we perform annotations on the publicly available AnnoMI dataset to collect multimodal information, including text, audio, facial expressivity, and body expressivity. Furthermore, we identify the most important modalities in the decision-making process, providing valuable insights into the interplay of different modalities during a MI conversation.< Réduire

Mots clés en anglais

Change talk

Multimodality

Interpretable

URI

https://oskar-bordeaux.fr/handle/20.500.12278/204128

DOI

http://dx.doi.org/10.1109/fg59268.2024.10581979

Project ANR

Entrainement d'aptitudes sociales affectives personnalisées et adaptées avec des agents culturels virtuels - ANR-19-JSTS-0001
adaPtAtion de l'iNtelligence artificielle pOur l'inteRActiOn homme-MAchine - ANR-20-IADJ-0008

Unités de recherche

SANPSY (Sommeil, Addiction, Neuropsychiatrie) - UMR 6033