Sommelier: 可扩展的开放式多轮音频预处理用于全双工语音语言模型
Sommelier: Scalable Open Multi-turn Audio Pre-processing for Full-duplex Speech Language Models
AI总结：
为全双工语音语言模型设计了一种鲁棒且可扩展的开源数据处理流程，以解决自然对话中的重叠和背通道问题，提升多说话人对话数据的质量和可用性。
摘要：
随着AI范式从基于文本的LLM转向语音语言模型（SLMs），对能够实现实时自然人机交互的全双工系统的需求日益增长。然而，此类模型的发展受到高质量多说话人对话数据稀缺的限制，现有大规模资源多为单说话人或体积有限。为解决自然对话的复杂动态，如重叠和背通道，标准处理流程面临说话人识别错误和ASR幻觉问题。为此，我们提出了一种鲁棒且可扩展的开源数据处理流程，专为全双工模型设计。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
