2017
ACL
ACL 2017
Understanding and Predicting Empathic Behavior in Counseling Therapy
Abstract
AbstractCounselor empathy is associated with better outcomes in psychology and behavioral counseling. In this paper, we explore several aspects pertaining to counseling interaction dynamics and their relation to counselor empathy during motivational interviewing encounters. Particularly, we analyze aspects such as participants’ engagement, participants’ verbal and nonverbal accommodation, as well as topics being discussed during the conversation, with the final goal of identifying linguistic and acoustic markers of counselor empathy. We also show how we can use these findings alongside other raw linguistic and acoustic features to build accurate counselor empathy classifiers with accuracies of up to 80%.
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Interdisciplinary Bridge
— Artificial Intelligence and Healthcare & Medicine and Interdisciplinary and Machine Learning
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Trend Setter
— Mental Health
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Keyword Pioneer
— counselor empathy
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Hot Topic Early Bird
— linguistic feature
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Human-AI Interaction
Machine Learning > Core Methods > Classification
Healthcare & Medicine > Clinical > Mental Health
Interdisciplinary > Social > Affective Computing
Machine Learning > Learning Types > Multi-Modal Learning
Machine Learning > Learning Types > Classification
Artificial Intelligence > Core AI > Speech Processing