Annotating and modeling empathy in spoken conversations

What is it about?

Empathy, as defined in behavioral sciences, expresses the ability of human be- ings to recognize, understand and react to emotions, attitudes and beliefs of others. 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 human-human dyadic 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. The automatic classification system was evaluated on call center conversations.

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Why is it important?

The ability to detect empathy is very useful in all those human tasks that are geared towards the assessment of human interactions (e.g., teacher-student, doctor-patient, customer-operator etc.). In addition, training intelligent agents to understand human emotions is an interesting problem.

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