Paper · arXiv 2603.25750
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
Full-duplex speech language models require high-quality multi-speaker conversational data, which is scarce, necessitating a robust open-source data processing pipeline to address challenges in natural dialogue dynamics and system accuracy.
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 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.