ICASSP 2026语音对话挑战：在LLM时代评估类人语音对话系统
The ICASSP 2026 HumDial Challenge: Benchmarking Human-like Spoken Dialogue Systems in the LLM Era
AI总结：
ICASSP 2026发起HumDial挑战，通过大规模数据集评估语音对话系统在情感智能和全双工交互方面的类人能力。
摘要：
在大型语言模型（LLMs）快速发展的推动下，特别是音频LLMs和全模型，语音对话系统显著发展，逐渐缩小了人机交互与人机交互之间的差距。实现真正"类人"的交流需要双重能力：情感智能以感知和共鸣用户的情绪状态，以及稳健的交互机制以导航动态自然的对话流程，例如实时轮流说话。因此，我们在ICASSP 2026上发起了第一个类人语音对话系统挑战（HumDial），以评估这些双重能力。依托一个大规模的数据集，该倡议在两个轨道上建立了公平的评估平台：（1）情感智能，针对长期情绪理解和共情生成；（2）全双工交互，系统评估在"边听边说"条件下实时决策能力。本文总结了数据集、轨道配置和最终结果。
英文摘要：
Driven by the rapid advancement of Large Language Models (LLMs), particularly Audio-LLMs and Omni-models, spoken dialogue systems have evolved significantly, progressively narrowing the gap between human-machine and human-human interactions. Achieving truly ``human-like'' communication necessitates a dual capability: emotional intelligence to perceive and resonate with users' emotional states, and robust interaction mechanisms to navigate the dynamic, natural flow of conversation, such as real-time turn-taking. Therefore, we launched the first Human-like Spoken Dialogue Systems Challenge (HumDial) at ICASSP 2026 to benchmark these dual capabilities. Anchored by a sizable dataset derived from authentic human conversations, this initiative establishes a fair evaluation platform across two tracks: (1) Emotional Intelligence, targeting long-term emotion understanding and empathetic generation; and (2) Full-Duplex Interaction, systematically evaluating real-time decision-making under `` listening-while-speaking'' conditions. This paper summarizes the dataset, track configurations, and the final results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
