A preliminary exploration of group social engagement level recognition in multiparty casual conversation

Abstract

Sensing human social engagement in dyadic or multiparty conversation is key to the design of decision strategies in conversational dialogue agents to decide suitable strategies in various human machine interaction scenarios. In this paper we report on studies we have carried out on the novel research topic about social group engagement in nontask oriented (casual) multiparty conversations. Fusion of hand-crafted acoustic and visual cues was used to predict social group engagement levels and was found to achieve higher results than using audio and visual cues separately.