Counseling is usually conducted through spoken conversation between a therapist and a client. The empathy level of therapist is a key indicator of outcomes. Presuming that therapist’s empathy expression is shaped by their past behavior and their perception of the client’s behavior, we propose a model to estimate the therapist empathy by considering both intrapersonal and interpersonal influences. These dynamic influences are captured by applying an attention mechanism to the therapist turn and the historical turns of both therapist and client. Our findings suggest that the integration of dynamic influences enhances empathy level estimation. The influence-derived embedding should constitute a minor portion of the target turn representation for optimal empathy estimation. The client’s turns (interpersonal influence) slightly surpass the therapist’s own turns (intrapersonal influence) in empathy estimation effectiveness. It is noted that concentrating exclusively on recent historical turns can significantly impact the estimation of therapist empathy.

Counseling is an activity of conversational speaking between a therapist and a client.Therapist empathy is an essential indicator of counseling quality and assessed subjectively by considering the entire conversation.This paper proposes to encode long counseling conversation using a hierarchical attention network.Conversations with extreme values of empathy rating are used to train a Siamese network based encoder with contrastive loss.Two-level attention mechanisms are applied to learn the importance weights of individual speaker turns and groups of turns in the conversation.Experimental results show that the use of contrastive loss is effective in encouraging the conversation encoder to learn discriminative embeddings that are related to therapist empathy.The distances between conversation embeddings positively correlate with the differences in the respective empathy scores.The learned conversation embeddings can be used to predict the subjective rating of therapist empathy.

In conversation-based psychotherapy, therapists use verbal techniques to help clients express thoughts and feelings, and change behavior.In particular, how well therapists convey empathy is an essential quality index of psychotherapy sessions and is associated with psychotherapy outcome.In this paper, we analyze the prosody of therapist speech and attempt to associate the therapist's speaking style with subjectively perceived empathy.An automatic speech and text processing system is developed to segment long recordings of psychotherapy sessions into pause-delimited utterances with text transcriptions.Data-driven clustering is applied to the utterances from different therapists in multiple sessions.For each cluster, a typological representation of utterance genre is derived based on quantized prosodic feature parameters.Prominent speaking styles of the therapist can be observed and interpreted from salient utterance genres that are correlated with empathy.Using the salient utterance genres, an accuracy of 71% is achieved in classifying psychotherapy sessions into "high" and "low" empathy level.Analysis of results suggests that empathy level tends to be (1) low if therapists speak long utterances slowly or speak short utterances quickly; and (2) high if therapists talk to clients with a steady tone and volume.

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 72.1% 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 2 to 6 consecutive turns should be considered together to provide useful clues for detecting empathy, and the observer tends to take the whole session into consideration when rating the therapist empathy, instead of relying on a few specific speaker turns.