Multimodal Analysis of Client Behavioral Change Coding in Motivational Interviewing

Abstract

Motivational Interviewing (MI) is a widely disseminated and effective therapeutic approach for behavioral disorder treatment. Over the past decade, MI research has identified client language as a central mediator between therapist skills and subsequent behavior change. Specifically, in-session client language referred to as change talk (CT; personal arguments for change) or sustain talk (ST; personal argument against changing the status quo) has been directly related to post-session behavior change. Despite the prevalent use of MI and extensive studies of MI underlying mechanisms, most existing studies focus on the linguistic aspect of MI, especially of client change talk and sustain talk and how they as a mediator influence the outcome of MI. In this study, we perform statistical analyses on acoustic behavior descriptors to test their discriminatory powers. Then we utilize multimodality by combining acoustic features with linguistic features to improve the accuracy of client change talk prediction. Lastly, we investigate into our trained model to understand what features inform the model about client utterance class and gain insights into the nature of MISC codes.