Recent advances in spoken dialogue systems have brought increased attention to human-like full-duplex voice interactions. However, our comprehensive review of this field reveals several challenges, including the difficulty in obtaining training data, catastrophic forgetting, and limited scalability. In this work, we propose SoulX-Duplug, a plug-and-play streaming state prediction module for full-duplex spoken dialogue systems. By jointly performing streaming ASR, SoulX-Duplug explicitly leverages textual information to identify user intent, effectively serving as a semantic VAD. To promote fair evaluation, we introduce SoulX-Duplug-Eval, extending widely used benchmarks with improved bilingual coverage. Experimental results show that SoulX-Duplug enables low-latency streaming dialogue state control, and the system built upon it outperforms existing full-duplex models in overall turn management and latency performance. We have open-sourced SoulX-Duplug and SoulX-Duplug-Eval.
Cite
@article{arxiv.2603.14877,
title = {SoulX-Duplug: Plug-and-Play Streaming State Prediction Module for Realtime Full-Duplex Speech Conversation},
author = {Ruiqi Yan and Wenxi Chen and Zhanxun Liu and Ziyang Ma and Haopeng Lin and Hanlin Wen and Hanke Xie and Jun Wu and Yuzhe Liang and Yuxiang Zhao and Pengchao Feng and Jiale Qian and Hao Meng and Yuhang Dai and Shunshun Yin and Ming Tao and Lei Xie and Kai Yu and Xinsheng Wang and Xie Chen},
journal= {arXiv preprint arXiv:2603.14877},
year = {2026}
}