Manual annotation of human behaviors with domain specific codes is a primary method of research and treatment fidelity evaluation in psychotherapy. However, manual annotation has a prohibitively high cost and does not scale to coding large amounts of psychotherapy session data. In this paper, we present a case study of modeling therapist language in addiction counseling, and propose an automatic coding approach. The task objective is to code therapist utterances with domain specific codes. We employ Recurrent Neural Networks (RNNs) to predict these behavioral codes based on session transcripts. Experiments show that RNNs outperform the baseline method using Maximum Entropy models. The model with bi-directional Gated Recurrent Units and domain specific word embeddings achieved the highest overall accuracy. We also briefly discuss about client code prediction and comparison to previous work.
Cite as: Xiao, B., Can, D., Gibson, J., Imel, Z.E., Atkins, D.C., Georgiou, P., Narayanan, S.S. (2016) Behavioral Coding of Therapist Language in Addiction Counseling Using Recurrent Neural Networks. Proc. Interspeech 2016, 908-912, doi: 10.21437/Interspeech.2016-1560
@inproceedings{xiao16_interspeech,
author={Bo Xiao and Doğan Can and James Gibson and Zac E. Imel and David C. Atkins and Panayiotis Georgiou and Shrikanth S. Narayanan},
title={{Behavioral Coding of Therapist Language in Addiction Counseling Using Recurrent Neural Networks}},
year=2016,
booktitle={Proc. Interspeech 2016},
pages={908--912},
doi={10.21437/Interspeech.2016-1560}
}