Annotating and Modeling Empathy in Spoken Conversations
This addresses the challenge of measuring empathy in behavioral analysis for applications like call center monitoring, though it appears incremental in applying existing multimodal analysis techniques to this specific domain.
The authors tackled the problem of automatically recognizing empathy in spoken conversations by proposing an annotation scheme and developing a classification system using lexical and acoustic features. Their system achieved significantly better performance than baseline on call center conversations.
Empathy, as defined in behavioral sciences, expresses the ability of human beings to recognize, understand and react to emotions, attitudes and beliefs of others. The lack of an operational definition of empathy makes it difficult to measure it. In this paper, we address two related problems in automatic affective behavior analysis: the design of the annotation protocol and the automatic recognition of empathy from spoken conversations. We propose and evaluate an annotation scheme for empathy inspired by the modal model of emotions. The annotation scheme was evaluated on a corpus of real-life, dyadic spoken conversations. In the context of behavioral analysis, we designed an automatic segmentation and classification system for empathy. Given the different speech and language levels of representation where empathy may be communicated, we investigated features derived from the lexical and acoustic spaces. The feature development process was designed to support both the fusion and automatic selection of relevant features from high dimensional space. The automatic classification system was evaluated on call center conversations where it showed significantly better performance than the baseline.