用对话模型让机器人更自然地轮流说话，减少停顿和打断。
Applying General Turn-taking Models to Conversational Human-Robot Interaction
- 引入通用对话轮换模型，无需微调即可预测发言时机。
- 实验显示响应延迟减少，用户对轮换体验的满意度显著提升。
- 适合研究人机对话交互、希望提升对话流畅性的开发者。
对话中的轮换是交流的基础，但当前人机交互系统多依赖简单的静默判断模型，导致不自然的停顿与打断。本文首次探究通用轮换模型（TurnGPT 和语音活动投影，VAP）在人机对话中的应用。这些模型基于人类对话数据，通过自监督学习训练，无需领域微调。我们提出将二者协同使用的方法，以预测机器人何时应准备回应、切入对话或处理打断。在包含39名成人的受控实验中，使用Furhat机器人结合大语言模型进行自主回复生成，结果表明参与者更偏好新系统，且响应延迟和打断次数均显著降低。
原文摘要 · Abstract (English)
Turn-taking is a fundamental aspect of conversation, but current Human-Robot Interaction (HRI) systems often rely on simplistic, silence-based models, leading to unnatural pauses and interruptions. This paper investigates, for the first time, the application of general turn-taking models, specifically TurnGPT and Voice Activity Projection (VAP), to improve conversational dynamics in HRI. These models are trained on human-human dialogue data using self-supervised learning objectives, without requiring domain-specific fine-tuning. We propose methods for using these models in tandem to predict when a robot should begin preparing responses, take turns, and handle potential interruptions. We evaluated the proposed system in a within-subject study against a traditional baseline system, using the Furhat robot with 39 adults in a conversational setting, in combination with a large language model for autonomous response generation. The results show that participants significantly prefer the proposed system, and it significantly reduces response delays and interruptions.
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