An Utterance Attitude Model in Human-Agent Communication: From Good Turn-taking to Better Human-Agent Understanding

摘要

In this study, we discuss a novel expression and comprehension model of the utterance attitude of speaking/hearing during conversations. Humans who participate in conversation display these implicit and explicit attitudes, and use them to understand the other participants in advance of turn-taking. We design abstract animated agents that mimic human turn-taking in conversations to confirm the validity of our model. The subjective evaluation tests show that the expressions of the agents are understandable. The model may facilitate turn-taking in human-agent interaction.