PeerJ Computer Science (Apr 2016)

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

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

DOI
https://doi.org/10.7717/peerj-cs.59
Journal volume & issue
Vol. 2
p. e59

Abstract

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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.

Keywords