Can we detect speakers' empathy?: A real-life case study

Abstract

In the context of automatic behavioral analysis, we aim to classify empathy in human-human spoken conversations. Empathy underlies to the human ability to recognize, understand and to react to emotions, attitudes, and beliefs of others. While empathy and its different manifestations (e.g., sympathy, compassion) have been widely studied in psychology, very little has been done in the computational research literature. In this paper, we present a case study where we investigate the occurrences of empathy in call-centers human-human conversations. In order to propose an operational definition of empathy, we adopt the modal model of emotions, where the appraisal processes of the unfolding of emotional states are modeled sequentially. We have designed a binary classification system to detect the presence of empathic manifestations in spoken conversations. The automatic classification system has been evaluated using spoken conversations by exploiting and comparing performances of the lexical, acoustic and psycholinguistic features.