Full-Duplex Interaction in Spoken Dialogue Systems: A Comprehensive Study from the ICASSP 2026 HumDial Challenge
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Abstract (arXiv)
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.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Apr 2026.
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Citing papers per year: 2026: 12.
12 papers.
- 2026IHBench: Evaluating Post-Interruption Recovery in Voice Agents with Structured Workflowscited by 5
- 2026MSI-Bench: Evaluating Multi-Speaker Voice Interaction for Collaborative AI Agentscited by 3
- 2026Omni-DuplexEval: Evaluating Real-time Duplex Omni-modal Interactioncited by 2
- 2026Overcoming State Inertia in Full-Duplex Spoken Language Models via Activation Steeringcited by 2
- 2026DuplexWorld: Can voice agents help you get through the day?cited by 2
- 2026MP-Bench: Evaluating Voice Agents as a Multiparty Conversation Participantcited by 2
- 2026Same Words, Different Actions: Paired Turn-Taking Evaluation under Rewritten Dialogue Contextscited by 1
- 2026Full-Duplex Speech Models Take the Floor When Asked, Not When Neededcited by 1
- 2026When Patients Cut In: Extending Clinical Conversational AI Safety to Interruptionscited by 0
- 2026Continue, Adapt, or Yield: In-Turn Adaptation to Overlapping Speech in Full-Duplex Agentscited by 0
- 2026DuplexAct-Bench: Broadening Full-Duplex Speech Evaluation toward Proactive Interaction across Diverse Behavioral Requirementscited by 0
- 2026DuplexAgent-RSI: Recursive Harness Improvement for Full-Duplex Voice Agent Collaborationcited by 0
- 2026DuplexAgent-RSI: Recursive Harness Improvement for Full-Duplex Voice Agent Collaboration8 Oct 2026
- 2026DuplexAct-Bench: Broadening Full-Duplex Speech Evaluation toward Proactive Interaction across Diverse Behavioral Requirements30 Sep 2026
- 2026MSI-Bench: Evaluating Multi-Speaker Voice Interaction for Collaborative AI Agents21 Sep 2026
- 2026Full-Duplex Speech Models Take the Floor When Asked, Not When Needed17 Sep 2026
- 2026Same Words, Different Actions: Paired Turn-Taking Evaluation under Rewritten Dialogue Contexts15 Sep 2026
- 2026Continue, Adapt, or Yield: In-Turn Adaptation to Overlapping Speech in Full-Duplex Agents11 Sep 2026
- 2026MP-Bench: Evaluating Voice Agents as a Multiparty Conversation Participant11 Sep 2026
- 2026When Patients Cut In: Extending Clinical Conversational AI Safety to Interruptions29 Aug 2026
- 2026DuplexWorld: Can voice agents help you get through the day?11 Aug 2026
- 2026IHBench: Evaluating Post-Interruption Recovery in Voice Agents with Structured Workflows17 Jun 2026
- 2026Overcoming State Inertia in Full-Duplex Spoken Language Models via Activation Steering9 Jun 2026
- 2026Omni-DuplexEval: Evaluating Real-time Duplex Omni-modal Interaction17 May 2026