Pardon the Interruption: Managing Turn-Taking through Overlap Resolution in Embodied Artificial Agents

Abstract

Speech overlap is a common phenomenon in natural conversation and in taskoriented interactions.As human-robot interaction (HRI) becomes more sophisticated, the need to effectively manage turntaking and resolve overlap becomes more important.In this paper, we introduce a computational model for speech overlap resolution in embodied artificial agents.The model identifies when overlap has occurred and uses timing information, dialogue history, and the agent's goals to generate context-appropriate behavior.We implement this model in a Nao robot using the DIARC cognitive robotic architecture.The model is evaluated on a corpus of task-oriented human dialogue, and we find that the robot can replicate many of the most common overlap resolution behaviors found in the human data.