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arXiv · 2509.20410

Phoenix-VAD: Streaming Semantic Endpoint Detection for Full-Duplex Speech Interaction

Weijie Wu·Wenhao Guan·Kaidi Wang·Peijie Chen·Zhuanling Zha·Junbo Li·Jun Fang·Lin Li·Qingyang Hong
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Abstract

Spoken dialogue models have significantly advanced intelligent human-computer interaction, yet they lack a plug-and-play full-duplex prediction module for semantic endpoint detection, hindering seamless audio interactions. In this paper, we introduce Phoenix-VAD, an LLM-based model that enables streaming semantic endpoint detection. Specifically, Phoenix-VAD leverages the semantic comprehension capability of the LLM and a sliding window training strategy to achieve reliable semantic endpoint detection while supporting streaming inference. Experiments on both semantically complete and incomplete speech scenarios indicate that Phoenix-VAD achieves excellent and competitive performance. Furthermore, this design enables the full-duplex prediction module to be optimized independently of the dialogue model, providing more reliable and flexible support for next-generation human-computer interaction.

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Weijie Wu, Wenhao Guan, Kaidi Wang, Peijie Chen, Zhuanling Zha, Junbo Li, Jun Fang, Lin Li, Qingyang Hong. 2025-09-24. Phoenix-VAD: Streaming Semantic Endpoint Detection for Full-Duplex Speech Interaction. https://arxiv.org/abs/2509.20410

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