Dynamic Prompting Improves Turn-taking in Embodied Spoken Dialogue Systems
Published in ROMAN' 25, 2025
A humanoid-robot SDS improves turn-taking by dynamically adjusting prompts to an audio LLM based on real-time floor perception (who’s speaking/listening). The LLM’s output drives both speech and floor-state updates, letting the system signal its stance. In robot interviews, this dynamic prompting outperformed static prompts: more accurate floor transitions, better signaling, fewer interruptions, smoother dialogue
Recommended citation: Yifan Shen, Dingdong Liu, Xiaoyu Mo, Fugee Tsung, Xiaojuan Ma, Bertram E. Shi
Recommended citation: Yifan Shen, Dingdong Liu, Xiaoyu Mo, Fugee Tsung, Xiaojuan Ma, Bertram E. Shi
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