EVAC 2024 – Empathic Virtual Agent Challenge: Appraisal-based Recognition of Affective States
Abstract
As autonomous interactive agents become increasingly prevalent, it is crucial for these virtual agents to understand and respond to both our verbal content and emotions, enabling deeper interactions. Despite significant advancements in the automatic recognition and understanding of human speech, challenges remain in accurately identifying and addressing the nuances of human emotions, hindering the development of more empathic virtual agents. We believe that empathic virtual agents should excel in three key tasks: (i) recognising spontaneous emotional expressions alongside understanding verbal content, (ii) generating timely and appropriate responses, and (iii) providing insightful feedback while comprehending user responses. To advance the development of empathic agents, we introduce the first Empathic Virtual Agent Challenge (EVAC). The inaugural edition focuses on robustly recognising spontaneous human expressions during interactions with a virtual agent, using the newly introduced THERADIA WoZ dataset. This paper provides an overview of the baseline systems operated on the pseudonymised version of the corpus on the two following modeling tasks: core affect presence and intensity, and appraisal based dimensions.
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