Motivational interviewing is a goal-oriented psychotherapy, employed in cases such as addiction, that aims to help clients explore and resolve their ambivalence about their problem. In motivational interviewing, it is desirable for the counselor to communicate empathy towards the client to promote better therapy outcomes. In this paper, we propose a deep neural network (DNN) system for predicting counselors’ session level empathy ratings from transcripts of the interactions. First, we train a recurrent neural network mapping the text of each speaker turn to a set of task-specific behavioral acts that represent local dynamics of the client-counselor interaction. Subsequently, this network is used to initialize lower layers of a deep network predicting session level counselor empathy rating. We show that this method outperforms training the DNN end-to-end in a single stage and also outperforms a baseline neural network model that attempts to predict empathy ratings directly from text without modeling turn level behavioral dynamics.
Cite as: Gibson, J., Can, D., Xiao, B., Imel, Z.E., Atkins, D.C., Georgiou, P., Narayanan, S.S. (2016) A Deep Learning Approach to Modeling Empathy in Addiction Counseling. Proc. Interspeech 2016, 1447-1451, doi: 10.21437/Interspeech.2016-554
@inproceedings{gibson16_interspeech,
author={James Gibson and Doğan Can and Bo Xiao and Zac E. Imel and David C. Atkins and Panayiotis Georgiou and Shrikanth S. Narayanan},
title={{A Deep Learning Approach to Modeling Empathy in Addiction Counseling}},
year=2016,
booktitle={Proc. Interspeech 2016},
pages={1447--1451},
doi={10.21437/Interspeech.2016-554}
}