[3Q1-OS-19a] Affective Computing

[3Q1-OS-19a-03] Detecting Change Talk using Language, Face, and Audio Information in Motivational Interviewing

〇Tomoya Tanaka¹, Shareef Kalluri Babu², Tatsuya Sakato³, Yukiko Nakano³ (1. Graduate School of Science and Technology, Seikei University, 2. Seikei University, 3. Seikei University Faculty of Science and Technology)

Keywords:Motivational Interviewing, Multimodal, Classification

Motivational Interviewing (MI) is a counseling technique that aims to elicit the Client's (CL) own reasons for behavior change. In MI, positive statements by CL are defined as Change Talk (CT). Previous studies have shown that CL with more CT are more motivated to change their behavior than CL with fewer CT. Other studies have defined the classification of CL utterance labels as a two-class classification problem between CT and Not-CT, and have proposed models for detecting CT using multimodal models of language and facial information. However, there has been no research on CT detection using language, face, and audio information. In this study, we propose a CT detection model that adds audio information to multimodal models using language and facial information. Experimental results show that the addition of audio information does not significantly improve the performance. We also found that weighting by utterance length is effective for Audio information.