Assessing empathy using static and dynamic behavior models based on therapist's language in addiction counseling

Empathy by the counselor is an important measure of treatment quality in psychotherapy. It is a behavioral process that involves understanding and sharing the experiences and emotions of a person over the course of an interaction. While a complex phenomenon, human behavior can at moments be perceived as strongly empathetic or non-empathetic. Currently, manual coding of behavior and behavioral signal processing models of empathy often pose the unnatural assumption that empathy is constant throughout the interaction. In this work we investigate two models: Static Behavior Model (SBM) that assumes a fixed degree of empathy throughout an interaction; and a contextdependent Dynamic Behavior Model (DBM), which assumes a Hidden Markov Model, allowing transitions between highand lowempathy states. Through the non-causal human perception mechanisms, these states can be perceived and integrated as highor lowgestalt empathy. We show that the DBM performs better than the SBM, while as a byproduct, generating local labels that may be of use to domain experts. We also demonstrate the robustness of both SBM and DBM to transcription errors stemming from ASR rather than human transcriptions. Our results suggest that empathy manifests itself in different forms over time and is best captured by contextdependent models.