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Seeing and hearing what has not been said: a multimodal client behavior classifier in Motivational Interviewing with interpretable fusion
Language
EN
Communication dans un congrès
This item was published in
2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG), 2024-05-28, Istanbul. p. 1-9
IEEE
English Abstract
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 ...Read more >
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.Read less <
English Keywords
Change talk
Multimodality
Interpretable
ANR Project
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
adaPtAtion de l'iNtelligence artificielle pOur l'inteRActiOn homme-MAchine - ANR-20-IADJ-0008