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arXiv · 2509.22243

FLEXI: Benchmarking Full-duplex Human-LLM Speech Interaction

Yuan Ge·Saihan Chen·Jingqi Xiao·Xiaoqian Liu·Tong Xiao·Yan Xiang·Zhengtao Yu·Jingbo Zhu
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Abstract

Full-Duplex Speech-to-Speech Large Language Models (LLMs) are foundational to natural human-computer interaction, enabling real-time spoken dialogue systems. However, benchmarking and modeling these models remains a fundamental challenge. We introduce FLEXI, the first benchmark for full-duplex LLM-human spoken interaction that explicitly incorporates model interruption in emergency scenarios. FLEXI systematically evaluates the latency, quality, and conversational effectiveness of real-time dialogue through six diverse human-LLM interaction scenarios, revealing significant gaps between open source and commercial models in emergency awareness, turn terminating, and interaction latency. Finally, we suggest that next token-pair prediction offers a promising path toward achieving truly seamless and human-like full-duplex interaction.

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Yuan Ge, Saihan Chen, Jingqi Xiao, Xiaoqian Liu, Tong Xiao, Yan Xiang, Zhengtao Yu, Jingbo Zhu. 2025-09-26. FLEXI: Benchmarking Full-duplex Human-LLM Speech Interaction. https://arxiv.org/abs/2509.22243

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