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Sommelier: Scalable Open Multi-turn Audio Pre-processing for Full-duplex Speech Language Models

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arXiv:2603.25750v1 Announce Type: cross Abstract: As the paradigm of AI shifts from text-based LLMs to Speech Language Models (SLMs), there is a growing demand for full-duplex systems capable of real-time, natural human-computer interaction. However, the development of such models is constrained by the scarcity of high-quality, multi-speaker conversational data, as existing large-scale resources are predominantly single-speaker or limited in volume. Addressing the complex dynamics of natural dia

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Computer Science > Sound [Submitted on 20 Mar 2026] Sommelier: Scalable Open Multi-turn Audio Pre-processing for Full-duplex Speech Language Models Kyudan Jung, Jihwan Kim, Soyoon Kim, Jeongoon Kim, Jaegul Choo, Cheonbok Park As the paradigm of AI shifts from text-based LLMs to Speech Language Models (SLMs), there is a growing demand for full-duplex systems capable of real-time, natural human-computer interaction. However, the development of such models is constrained by the scarcity of high-quality, multi-speaker conversational data, as existing large-scale resources are predominantly single-speaker or limited in volume. Addressing the complex dynamics of natural dialogue, such as overlapping and back-channeling remains a challenge, with standard processing pipelines suffering from diarization errors and ASR hallucinations. To bridge this gap, we present a robust and scalable open-source data processing pipeline designed for full-duplex model. Comments: 34 pages, 7 figures, 11 tables Subjects: Sound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS) Cite as: arXiv:2603.25750 [cs.SD] (or arXiv:2603.25750v1 [cs.SD] for this version) https://doi.org/10.48550/arXiv.2603.25750 Focus to learn more Submission history From: Kyudan Jung [view email] [v1] Fri, 20 Mar 2026 09:10:43 UTC (3,412 KB) Access Paper: view license Current browse context: cs.SD < prev | next > new | recent | 2026-03 Change to browse by: cs cs.AI eess eess.AS References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

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