Work

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

Year: 2026

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2603.25750

Authors: Jung, Kyudan, Kim, Jihwan, Kim, Soyoon, Kim, Jeonghoon, Choo, Jaegul, Park, Cheonbok

Institutions: Korea Advanced Institute of Science and Technology, NAVER Cloud (South Korea)

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7143443580

Cites:

Cited by:

Related to: