# Feature Fusion Strategies for End-to-End Evaluation of Cognitive Behavior Therapy Sessions

arXiv: https://arxiv.org/abs/2005.07809 · HTML: https://mlsanity.com/paper/2005.07809/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2020.

## Built on by (most cited first, 6 of 6)

- [Automated Quality Assessment of Cognitive Behavioral Therapy Sessions Through Highly Contextualized Language Representations](https://mlsanity.com/paper/2102.11573/) (2021)
- [Automated Evaluation Of Psychotherapy Skills Using Speech And Language Technologies](https://mlsanity.com/paper/2102.11265/) (2021)
- [Seeing and hearing what has not been said; A multimodal client behavior classifier in Motivational Interviewing with interpretable fusion](https://mlsanity.com/paper/2309.14398/) (2023)
- [Fine-Tuning Large Audio-Language Models with LoRA for Precise Temporal Localization of Prolonged Exposure Therapy Elements](https://mlsanity.com/paper/2506.09707/) (2025)
- [An Automated Quality Evaluation Framework of Psychotherapy Conversations with Local Quality Estimates](https://mlsanity.com/paper/2106.07922/) (2021)
- [Local dynamic mode of Cognitive Behavioral Therapy](https://mlsanity.com/paper/2205.09752/) (2022)

## Abstract (arXiv)

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.