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arXiv · 2601.05564

The ICASSP 2026 HumDial Challenge: Benchmarking Human-like Spoken Dialogue Systems in the LLM Era

Zhixian Zhao·Shuiyuan Wang·Guojian Li·Hongfei Xue·Chengyou Wang·Shuai Wang·Longshuai Xiao·Zihan Zhang·Hui Bu·Xin Xu·Xinsheng Wang·Hexin Liu·Eng Siong Chng·Hung-yi Lee·Lei Xie
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Abstract

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.

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Zhixian Zhao, Shuiyuan Wang, Guojian Li, Hongfei Xue, Chengyou Wang, Shuai Wang, Longshuai Xiao, Zihan Zhang, Hui Bu, Xin Xu, Xinsheng Wang, Hexin Liu, Eng Siong Chng, Hung-yi Lee, Lei Xie. 2026-01-09. The ICASSP 2026 HumDial Challenge: Benchmarking Human-like Spoken Dialogue Systems in the LLM Era. https://arxiv.org/abs/2601.05564

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