提升对话机器人在嘈杂环境中的响应速度，让交互更自然。
A Noise-Robust Turn-Taking System for Real-World Dialogue Robots: A Field Experiment
- 用Transformer架构设计抗噪语音活动预测模型
- 实地实验显示响应延迟显著降低，双方反应更快
- 适合需要真实场景部署的对话机器人研发者
话语交替是人机交互的关键，直接影响对话流畅性和用户参与度。尽管先前研究已在受控环境中探索话语交替模型，但其在真实场景下的鲁棒性仍待深入。本研究提出一种基于Transformer架构的抗噪语音活动投影（VAP）模型，以提升对话机器人的实时话语交替能力。为评估该系统有效性，我们在商场开展了实地实验，对比了VAP系统与传统云端语音识别系统的性能。分析涵盖主观用户评价与客观行为数据。结果表明，所提系统显著降低了响应延迟，使机器人与用户均能更快回应，实现更自然的对话流程。主观评价也显示，更快的响应提升了交互体验。
原文摘要 · Abstract (English)
Turn-taking is a crucial aspect of human-robot interaction, directly influencing conversational fluidity and user engagement. While previous research has explored turn-taking models in controlled environments, their robustness in real-world settings remains underexplored. In this study, we propose a noise-robust voice activity projection (VAP) model, based on a Transformer architecture, to enhance real-time turn-taking in dialogue robots. To evaluate the effectiveness of the proposed system, we conducted a field experiment in a shopping mall, comparing the VAP system with a conventional cloud-based speech recognition system. Our analysis covered both subjective user evaluations and objective behavioral analysis. The results showed that the proposed system significantly reduced response latency, leading to a more natural conversation where both the robot and users responded faster. The subjective evaluations suggested that faster responses contribute to a better interaction experience.
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