Abstract

Cognitive Behavioral Therapy (CBT) is a goal-oriented psychotherapy for mental health concerns implemented in a conversational setting. The quality of a CBT session is typically assessed by trained human raters who manually assign pre-defined session-level behavioral codes. In this paper, we develop an end-to-end pipeline that converts speech audio to diarized and transcribed text and extracts linguistic features to code the CBT sessions automatically. We investigate both word-level and utterance-level features and propose feature fusion strategies to combine them. The utterance level features include dialog act tags as well as behavioral codes drawn from another well-known talk psychotherapy called Motivational Interviewing (MI). We propose a novel method to augment the word-based features with the utterance level tags for subsequent CBT code estimation. Experiments show that our new fusion …

Date

2021

Authors

Zhuohao Chen, Nikolaos Flemotomos, Victor Ardulov, Torrey A Creed, Zac E Imel, David C Atkins, Shrikanth Narayanan

Conference

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Pages

1836-1839

Publisher

IEEE