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

@article{Chen2021FeatureFS,
  title={Feature Fusion Strategies for End-to-End Evaluation of Cognitive Behavior Therapy Sessions},
  author={Zhuohao Chen and Nikolaos Flemotomos and Victor Ardulov and Torrey A. Creed and Zac E. Imel and David C. Atkins and Shrikanth S. Narayanan},
  journal={2021 43rd Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)},
  year={2021},
  pages={1836-1839}
}
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… 

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References

SHOWING 1-10 OF 39 REFERENCES

Language Features for Automated Evaluation of Cognitive Behavior Psychotherapy Sessions

TLDR
This work examines how linguistic features can be effectively used to develop an automatic competency rating tool for CBT, and suggests that a real-world system could be developed to automatically evaluate CBT sessions to assist training, supervision, or quality assurance of services.

A dialog act tagging approach to behavioral coding: a case study of addiction counseling conversations

TLDR
A computational approach to modeling and assessing the quality of MI sessions is examined and linear chain CRF models trained on coded session transcripts and Switchboard DAMSL dataset are used to predict utterance level behavioral codes as well as dialog acts.

Behavioral Coding of Therapist Language in Addiction Counseling Using Recurrent Neural Networks

TLDR
A case study of modeling therapist language in addiction counseling, and an automatic coding approach to code therapist utterances with domain specific codes is presented.

Using Prosodic and Lexical Information for Learning Utterance-level Behaviors in Psychotherapy

TLDR
It is demonstrated that prosodic features provide discriminative information relevant to the behavior task and show that they improve prediction when fused with automatically derived lexical features.

Multi-Label Multi-Task Deep Learning for Behavioral Coding

We propose a methodology for estimating human behaviors in psychotherapy sessions using multi-label and multi-task learning paradigms. We discuss the problem of behavioral coding in which data of

Scaling up the evaluation of psychotherapy: evaluating motivational interviewing fidelity via statistical text classification

TLDR
Preliminary, encouraging findings are demonstrated regarding the utility of statistical text classification in bridging this methodological gap in replicating human-based judgments of provider fidelity in one specific psychotherapy— motivational interviewing (MI).

Preparing Clients for Cognitive Behavioral Therapy: A Randomized Pilot Study of Motivational Interviewing for Anxiety

Although CBT is a well-supported treatment for anxiety, recovery rates and compliance with treatment procedures are less than optimal. Using adjunctive brief preparatory interventions may help

Multimodal Interaction Modeling of Child Forensic Interviewing

TLDR
Using a recently proposed definition of productivity, the dynamic systems modeling provides insight into the characteristics of interaction that are most relevant to effectively eliciting narrative and task-relevant information from a child.

Motivational Interviewing as an Adjunct to Cognitive Behavior Therapy for Anxiety Disorders: A Critical Review of the Literature.

Improving the Prediction of Therapist Behaviors in Addiction Counseling by Exploiting Class Confusions

TLDR
This work addresses the problem of joint prosodic and lexical behavioral annotation for addiction counseling by proposing two implementations of hierarchical classification, which uses the behavior confusion matrix to cluster similar classes and makes the prediction based on a tree structure.