Feature Fusion Strategies for End-to-End Evaluation of Cognitive Behavior Therapy Sessions
This work addresses the need for automated quality assessment in psychotherapy sessions, which is incremental as it builds on existing methods by combining features from different levels.
The paper tackled the problem of automatically coding Cognitive Behavioral Therapy (CBT) sessions by developing an end-to-end pipeline that converts speech audio to text and extracts linguistic features, proposing feature fusion strategies that outperform individual features and direct concatenation, with improvements from a sentence segmentation module.
Cognitive Behavioral Therapy (CBT) is a goal-oriented psychotherapy for mental health concerns implemented in a conversational setting with broad empirical support for its effectiveness across a range of presenting problems and client populations. 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 strategy outperforms all the studied features, both when used individually and when fused by direct concatenation. We also find that incorporating a sentence segmentation module can further improve the overall system given the preponderance of multi-utterance conversational turns in CBT sessions.