Emotion Unfolding and Affective Scenes: A Case Study in Spoken Conversations

The manifestation of human emotions evolves over time and space. Most of the work on affective computing research is limited to the association of context-free signal segments, such as utterances and images, to basic emotions. In this paper, we discuss the hypothesis that interpreting emotions requires a conceptual description of their dynamics within the context of their manifestations. We describe the unfolding of emotions through the proposed affective scene framework. Affective scenes are defined in terms of who first expresses the variation in their emotional state in a conversation, how this affects the other speaker's emotional appraisal and response, and which modifications occur from the initial through the final state of the scene. This conceptual framework is applied and evaluated on real human-human conversations drawn from call centers. We show that the automatic classification of affective scenes achieves more than satisfactory results and it benefits from acoustic, lexical and psycholinguistic features of the speech and linguistics signals.