Full-Duplex Interaction in Spoken Dialogue Systems: A Comprehensive Study from the ICASSP 2026 HumDial Challenge

TL;DR

This paper presents a comprehensive benchmark and dataset for evaluating full-duplex spoken dialogue systems capable of handling real-time interruptions and overlapping speech, advancing more natural human-like interactions.

Contribution

It introduces a new dataset, benchmark, and evaluation framework for full-duplex dialogue systems, addressing a key challenge in natural conversational AI.

Findings

The dataset captures real human conversations with interruptions and overlaps.

The HumDial-FDBench benchmark evaluates system performance in dynamic turn-taking.

A public leaderboard facilitates transparent comparison of dialogue models.

Abstract

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.