论文标题
用于评估治疗师同理心的分层注意网络
Hierarchical Attention Network for Evaluating Therapist Empathy in Counseling Session
论文作者
论文摘要
咨询通常采用治疗师和客户之间的口语对话形式。治疗师表达的移情水平被认为是咨询结果的重要质量因素。本文提出了一个分层的经常性网络，并结合了两级注意机制，以确定治疗师的同理心级别，仅从咨询会议中的对话演讲的声学特征中。实验结果表明，所提出的模型可以在将治疗师的移情水平分类为``高度''或``'''或``''或`'''中实现$ 72.1 \％$的准确性。发现治疗师和客户的讲话都在预测专家观察者主观评价的同理心水平。通过分析以高注意力重量分配的扬声器转弯，可以观察到，应将$ 2 $至$ 6 $连续的转弯考虑在一起，以提供有用的线索来检测同理心，并且观察者在对治疗师的同情心进行评估时倾向于考虑整个会议，而不是依靠几个特定的扬声器转弯。
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.
