Paper Info
Title
Feature Fusion Strategies for End-to-End Evaluation of Cognitive Behavior Therapy Sessions
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 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.
Year
DOI
Venue
2021
10.1109/EMBC46164.2021.9629694
2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC)
Keywords
DocType
Volume
Cognitive behavioral therapy, Motivational Interviewing, end-to-end evaluation, feature fusion strategies
Conference
2021
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Chen Zhuohao100.34
Flemotomos Nikolaos212.05
Ardulov Victor300.34
Creed Torrey A.400.34
Zac E Imel5135.53
David Atkins65512.28
Narayanan Shrikanth75558439.23