A technology prototype system for rating therapist empathy from audio recordings in addiction counseling.

David C. Atkins,Shrikanth S. Narayanan,Panayiotis Georgiou,Zac E. Imel,Bo Xiao,Chewei Huang

doi:10.7717/peerj-cs.59

David C. Atkins, Shrikanth S. Narayanan

Open Access

https://doi.org/10.7717/peerj-cs.59

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Abstract

Scaling up psychotherapy services such as for addiction counseling is a critical societal need. One challenge is ensuring quality of therapy, due to the heavy cost of manual observational assessment. This work proposes a speech technology-based system to automate the assessment of therapist empathy-a key therapy quality index-from audio recordings of the psychotherapy interactions. We designed a speech processing system that includes voice activity detection and diarization modules, and an automatic speech recognizer plus a speaker role matching module to extract the therapist's language cues. We employed Maximum Entropy models, Maximum Likelihood language models, and a Lattice Rescoring method to characterize high vs. low empathic language. We estimated therapy-session level empathy codes using utterance level evidence obtained from these models. Our experiments showed that the fully automated system achieved a correlation of 0.643 between expert annotated empathy codes and machine-derived estimations, and an accuracy of 81% in classifying high vs. low empathy, in comparison to a 0.721 correlation and 86% accuracy in the oracle setting using manual transcripts. The results show that the system provides useful information that can contribute to automatic quality insurance and therapist training.

Highlights

Addiction counseling is a type of psychotherapy, where the therapist aims to support changing the patient’s addictive behavior through face-to-face conversational interaction
We propose three methods for empathy level estimation based on language models representing high vs. low empathy, including using the Maximum Entropy model, the Maximum likelihood based model trained with human-generated transcripts, and a Maximum likelihood approach based on direct Automatic Speech Recognition (ASR) lattice rescoring
ORA-D—ASR decoding of therapist language with manual labels of speech segmentation and speaker roles, followed by empathy modeling on the decoded therapist language

Summary

Introduction

Addiction counseling is a type of psychotherapy, where the therapist aims to support changing the patient’s addictive behavior through face-to-face conversational interaction. Mental health care toward drug and alcohol abuse is essential to society. A national survey in the United States by the Substance Abuse and Mental Health Services Administration showed that there were 23.9 million illicit drug users in 2012. Only 2.5 million persons received treatment at a specialty facility (Substance Abuse and Mental Health Services Administration, 2013). Further to the gap between the provided addiction counseling and what is needed, it is challenging to evaluate millions of counseling cases regarding the quality of the therapy and the competence of the therapists. How to cite this article Xiao et al (2016), A technology prototype system for rating therapist empathy from audio recordings in addiction counseling.

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