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arXiv · 2509.14515

From Turn-Taking to Synchronous Dialogue: A Survey of Full-Duplex Spoken Language Models

Yuxuan Chen·Haoyuan Yu
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Abstract

True Full-Duplex (TFD) voice communication--enabling simultaneous listening and speaking with natural turn-taking, overlapping speech, and interruptions--represents a critical milestone toward human-like AI interaction. This survey comprehensively reviews Full-Duplex Spoken Language Models (FD-SLMs) in the LLM era. We establish a taxonomy distinguishing Engineered Synchronization (modular architectures) from Learned Synchronization (end-to-end architectures), and unify fragmented evaluation approaches into a framework encompassing Temporal Dynamics, Behavioral Arbitration, Semantic Coherence, and Acoustic Performance. Through comparative analysis of mainstream FD-SLMs, we identify fundamental challenges: synchronous data scarcity, architectural divergence, and evaluation gaps, providing a roadmap for advancing human-AI communication.

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Yuxuan Chen, Haoyuan Yu. 2025-09-18. From Turn-Taking to Synchronous Dialogue: A Survey of Full-Duplex Spoken Language Models. https://arxiv.org/abs/2509.14515

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