Hierarchical Attention Network for Evaluating Therapist Empathy in Counseling Session
Dehua Tao, Tan Lee, Harold Chui, Sarah Luk

TL;DR
This paper introduces a hierarchical attention-based neural network that analyzes acoustic speech features to accurately assess therapist empathy levels in counseling sessions, highlighting the importance of considering multiple speaker turns and entire sessions.
Contribution
The study presents a novel hierarchical recurrent network with two-level attention mechanisms for empathy detection solely from acoustic features, achieving 72.1% accuracy.
Findings
Both therapist and client speech contribute to empathy prediction.
Multiple consecutive turns (2-6) provide useful cues for detecting empathy.
Whole session context influences empathy ratings more than individual turns.
Abstract
Counseling typically takes the form of spoken conversation between a therapist and a client. The empathy level expressed by the therapist is considered to be an essential quality factor of counseling outcome. This paper proposes a hierarchical recurrent network combined with two-level attention mechanisms to determine the therapist's empathy level solely from the acoustic features of conversational speech in a counseling session. The experimental results show that the proposed model can achieve an accuracy of in classifying the therapist's empathy level as being ``high" or ``low". It is found that the speech from both the therapist and the client are contributing to predicting the empathy level that is subjectively rated by an expert observer. By analyzing speaker turns assigned with high attention weights, it is observed that to consecutive turns should be considered…
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Taxonomy
TopicsDigital Mental Health Interventions · Mental Health via Writing
