构建首个真实对话的全双工语音系统评测基准，支持实时打断与重叠对话。
Full-Duplex Interaction in Spoken Dialogue Systems: A Comprehensive Study from the ICASSP 2026 HumDial Challenge
- 基于真实人类对话构建双通道数据集，捕捉打断与重叠语流
- 提出HumDial-FDBench基准，评估系统在干扰下的对话连贯性
- 公开排行榜促进模型可复现对比，适合对话系统研发者
全双工交互，即说话人与听者可同时发言，是人类交流的关键特征，却常被传统语音对话系统所忽视。这些系统依赖严格的轮次机制，在动态对话中难以自然响应。ICASSP 2026人类对话系统挑战赛（HumDial Challenge）中的全双工交互赛道旨在推进该类系统的评估，提供处理实时打断、语音重叠和动态轮次协商的框架。我们引入一个全面的全双工语音对话系统评测基准，基于HumDial Challenge构建。释放高质量双通道真实人类对话数据集，涵盖打断、重叠语音与反馈机制。该数据集构成HumDial-FDBench基准，用于评估系统在保持对话流畅性前提下处理打断的能力。此外，建立公开排行榜，用于比较开源与专有模型表现，推动透明、可复现的评估。这些资源支持更快速、自适应、类人化对话系统的发展。
原文摘要 · Abstract (English)
Full-duplex interaction, where speakers and listeners converse simultaneously, is a key element of human communication often missing from traditional spoken dialogue systems. These systems, based on rigid turn-taking paradigms, struggle to respond naturally in dynamic conversations. The Full-Duplex Interaction Track of ICASSP 2026 Human-like Spoken Dialogue Systems Challenge (HumDial Challenge) aims to advance the evaluation of full-duplex systems by offering a framework for handling real-time interruptions, speech overlap, and dynamic turn negotiation. We introduce a comprehensive benchmark for full-duplex spoken dialogue systems, built from the HumDial Challenge. We release a high-quality dual-channel dataset of real human-recorded conversations, capturing interruptions, overlapping speech, and feedback mechanisms. This dataset forms the basis for the HumDial-FDBench benchmark, which assesses a system's ability to handle interruptions while maintaining conversational flow. Additionally, we create a public leaderboard to compare the performance of open-source and proprietary models, promoting transparent, reproducible evaluation. These resources support the development of more responsive, adaptive, and human-like dialogue systems.
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