{"ID":2826538,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.18706","arxiv_id":"2512.18706","title":"X-Talk: On the Underestimated Potential of Modular Speech-to-Speech Dialogue System","abstract":"We present X-Talk, an open-source framework that champions a decoupled, modular design for LLM-driven speech-to-speech (S2S) systems. While the dominant trend favors end-to-end (E2E) modeling to optimize information flow, these \"omni-models\" often struggle to balance the competing objectives of complex speech tasks within a single network. X-Talk challenges this paradigm by demonstrating that a systematically optimized cascaded pipeline can achieve sub-second latency without sacrificing modular flexibility. Our framework seamlessly integrates specialized front-end components (e.g., VAD, speech enhancement) and diverse understanding models (e.g., ASR, emotion, and environmental sound analysis) with LLM capabilities like retrieval-augmented generation (RAG) and tool use. By revitalizing the cascaded approach, X-Talk highlights the underestimated potential of modular S2S systems and provides a robust foundation for future research and applications.","short_abstract":"We present X-Talk, an open-source framework that champions a decoupled, modular design for LLM-driven speech-to-speech (S2S) systems. While the dominant trend favors end-to-end (E2E) modeling to optimize information flow, these \"omni-models\" often struggle to balance the competing objectives of complex speech tasks wit...","url_abs":"https://arxiv.org/abs/2512.18706","url_pdf":"https://arxiv.org/pdf/2512.18706v1","authors":"[\"Zhanxun Liu\",\"Yifan Duan\",\"Mengmeng Wang\",\"Pengchao Feng\",\"Haotian Zhang\",\"Xiaoyu Xing\",\"Yijia Shan\",\"Haina Zhu\",\"Yuhang Dai\",\"Chaochao Lu\",\"Xipeng Qiu\",\"Lei Xie\",\"Lan Wang\",\"Nan Yan\",\"Zilong Zheng\",\"Ziyang Ma\",\"Kai Yu\",\"Xie Chen\"]","published":"2025-12-21T11:50:32Z","proceeding":"cs.SD","tasks":"[\"cs.SD\"]","methods":"[\"RAG\",\"Large Language Model\"]","has_code":false}