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The ICASSP 2026 HumDial Challenge introduced a benchmark for full-duplex spoken dialogue systems to address the limitations of traditional turn-taking paradigms. A dual-channel dataset of real human conversations was collected, capturing interruptions, overlapping speech, and feedback mechanisms. The HumDial-FDBench benchmark assesses a system's ability to handle interruptions and maintain conversational flow, with a public leaderboard for transparent evaluation.
Current spoken dialogue systems struggle with the nuances of human conversation, but a new benchmark offers a path to more natural interactions by focusing on handling interruptions and overlapping speech.
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.
