Abstract

Motivational Interviewing (MI) is a goal-oriented psychotherapy, employed in cases such as addiction, which helps clients (ie, patients) explore and resolve their ambivalence about the problem at hand in a dialog setting. Measuring the counselor’s proficiency with MI has typically been assessed via behavioral coding-a time consuming, non-technological approach. This paper examines a computational approach to assessing the quality of MI. Specifically, we focus on a particular aspect of the counselor behavior–reflections–believed to be a critical indicator of MI therapy quality. We automatically tag reflection instances in a maximum entropy Markov modeling framework using several linguistic features with rich contextual information obtained from the session transcripts. We achieve an F-score of over 80% while gaining insight about the information sources as perceived by the trained annotators.

Date

2012

Authors

Dogan Can, Panayiotis G Georgiou, David C Atkins, Shrikanth S Narayanan

Journal

INTERSPEECH

Volume

2012

Pages

13th