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Multimodal Analysis and Assessment of Therapist Empathy in Motivational Interviews

Authors:

Joannalyn Delacruz,

Joshua D Woolley,

Stefan Scherer,

Mohammad SoleymaniAuthors Info & Claims

ICMI '23: Proceedings of the 25th International Conference on Multimodal Interaction

Pages 406 - 415

https://doi.org/10.1145/3577190.3614105

Published: 09 October 2023 Publication History

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Abstract

The quality and effectiveness of psychotherapy sessions are highly influenced by the therapists’ ability to meaningfully connect with clients. Automated assessment of therapist empathy provides cost-effective and systematic means of assessing the quality of therapy sessions. In this work, we propose to assess therapist empathy using multimodal behavioral data, i.e. spoken language (text) and audio in real-world motivational interviewing (MI) sessions for alcohol abuse intervention. We first study each modality (text vs. audio) individually and then evaluate a multimodal approach using different fusion strategies for automated recognition of empathy levels (high vs. low). Leveraging recent pre-trained models both for text (DistilRoBERTa) and speech (HuBERT) as strong unimodal baselines, we obtain consistent 2-3 point improvements in F1 scores with early and late fusion, and the highest absolute improvement of 6–12 points over unimodal baselines. Our models obtain F1 scores of 68% when only looking at an early segment of the sessions and up to 72% in a therapist-dependent setting. In addition, our results show that a relatively small portion of sessions, specifically the second quartile, is most important in empathy prediction, outperforming predictions on later segments and on the full sessions. Our analyses in late fusion results show that fusion models rely more on the audio modality in limited-data settings, such as in individual quartiles and when using only therapist turns. Further, we observe the highest misclassification rates for parts of the sessions with MI inconsistent utterances (20% misclassified by all models), likely due to the complex nature of these types of intents in relation to perceived empathy.

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Cited By

Han GLiu WHuang XBorsari B(2024)Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts2024 IEEE 12th International Conference on Healthcare Informatics (ICHI)10.1109/ICHI61247.2024.00057(392-401)Online publication date: 3-Jun-2024
https://doi.org/10.1109/ICHI61247.2024.00057

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Published In

cover image ACM Conferences

ICMI '23: Proceedings of the 25th International Conference on Multimodal Interaction

October 2023

858 pages

ISBN:9798400700552

DOI:10.1145/3577190

Editors:
Elisabeth André
University of Augsburg
,
Mohamed Chetouani
Sorbonne University
,
Dominique Vaufreydaz
Univ. Grenoble Alpes
,
Gale Lucas
USC Institute for Creative Technologies
,
Tanja Schultz
University of Bremen
,
Louis-Philippe Morency
Carnegie Mellon University
,
Alessandro Vinciarelli
University of Glasgow

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Published: 09 October 2023

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ICMI '23: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

October 9 - 13, 2023

Paris, France

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Cited By

Han GLiu WHuang XBorsari B(2024)Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts2024 IEEE 12th International Conference on Healthcare Informatics (ICHI)10.1109/ICHI61247.2024.00057(392-401)Online publication date: 3-Jun-2024
https://doi.org/10.1109/ICHI61247.2024.00057

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