SAGE-LD：通过模拟数据增强实现可扩展、泛化性强的端到端语言说话人分离
SAGE-LD: Towards Scalable and Generalizable End-to-End Language Diarization via Simulated Data Augmentation
AI总结：
本研究提出SAGE-LD神经语言分离模型，结合可学习查询架构与模拟语码转换数据预训练，解决数据稀缺问题，在多基准上实现23%-52%的性能提升，为语码转换语音技术奠基。
摘要：
本文提出了一种神经口语语言分离模型，该模型可在单一框架内支持无限制的语言范围。我们的方法整合了基于可学习查询、具备多语言感知能力的架构，并在模拟语码转换数据上进行大规模预训练。通过联合利用这两个组件，我们的方法克服了传统方法在数据稀缺和架构优化方面的局限，能有效泛化到不同环境下的真实多语言场景。实验结果表明，我们的方法在多个语言分离基准上取得了当前最优性能，相比先前方法实现了23%到52%的相对性能提升。我们认为，这项工作不仅推动了语言分离领域的研究，还为语码转换语音技术奠定了基础框架。
英文摘要：
In this paper, we present a neural spoken language diarization model that supports an unconstrained span of languages within a single framework. Our approach integrates a learnable query-based architecture grounded in multilingual awareness, with large-scale pretraining on simulated code-switching data. By jointly leveraging these two components, our method overcomes the limitations of conventional approaches in data scarcity and architecture optimization, and generalizes effectively to real-world multilingual settings across diverse environments. Experimental results demonstrate that our approach achieves state-of-the-art performance on several language diarization benchmarks, with a relative performance improvement of 23% to 52% over previous methods. We believe that this work not only advances research in language diarization but also establishes a foundational framework for code-switching speech technologies.
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。
