The quality and effectiveness of psychotherapy sessions are highly influenced by the therapists’ ability to lead the conversation with empathy and acceptance. Manual assessment of the quality of therapy sessions is labor-intensive and difficult to scale. In this paper, we propose a method for estimating session-level therapist empathy ratings for Motivational Interviewing (MI) using therapist language, which has applications in clinical assessment and training. We analyze different stages within therapy sessions to investigate the importance of each stage and its topics of conversation in estimating session-level therapist empathy. We perform experiments on two datasets of MI therapy sessions for alcohol use disorder with session-level empathy scores provided by expert annotators. We achieve average CCC (Concordance Correlation Coefficient) scores of 0.596 and 0.408 for estimating therapist empathy under therapist-dependent and therapist-independent evaluation settings. Our results suggest that therapist responses to client’s discussions on activities and experiences around the problematic behavior (in this case, alcohol abuse) along with the therapist’s usage of in-depth reflections, are the most significant factors in the perception of therapists’ empathy.

Motivational Interviewing (MI) is defined as a collaborative conversation style that evokes the client's own intrinsic reasons for behavioral change. In MI research, the clients' attitude (willingness or resistance) toward change as expressed through language, has been identified as an important indicator of their subsequent behavior change. Automated coding of these indicators provides systematic and efficient means for the analysis and assessment of MI therapy sessions. In this paper, we study and analyze behavioral cues in client language and speech that bear indications of the client's behavior toward change during a therapy session, using a database of dyadic motivational interviews between therapists and clients with alcohol-related problems. Deep language and voice encoders, \ie BERT and VGGish, trained on large amounts of data are used to extract features from each utterance. We develop a neural network to automatically detect the MI codes using both the clients' and therapists' language and clients' voice, and demonstrate the importance of semantic context in such detection. Additionally, we develop machine learning models for predicting alcohol-use behavioral outcomes of clients through language and voice analysis. Our analysis demonstrates that we are able to estimate MI codes using clients' textual utterances along with preceding textual context from both the therapist and client, reaching an F1-score of 0.72 for a speaker-independent three-class classification. We also report initial results for using the clients' data for predicting behavioral outcomes, which outlines the direction for future work.

Categorizing a patient's intentions during clinical interactions in general and within motivational interviewing specifically may improve decision making in clinical treatments.Within this paper, we propose a method that models the temporal flow of a conversation and the transition between topics by using domain adaptation on a clinical dialogue corpus comprising Motivational Interviewing (MI) sessions.We deploy Bi-LSTM and topic models jointly to learn theme shifts across different time stages within these hour-long MI sessions to assess the patient's intent to change their habits or to sustain them respectively.Our experiments show promising results and improvements after considering temporality in the classification task over our baseline.This result confirms and extends related literature that has manually identified that certain phases within MI sessions are more predictive of patient outcomes than others.

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.