Abstract
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.
Abstract (translated)
在这篇论文中，我们提出了一种神经口语语言身份识别模型，该模型在一个单一框架内支持无限制的语言跨度。我们的方法结合了一个基于多语种意识的可学习查询架构，并在模拟代码转换数据上进行了大规模预训练。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性，并能够有效地推广到各种环境下的现实世界多语言设置中。实验结果表明，我们所提出的方法在几种语言身份识别基准测试中实现了最先进的性能，在相对性能方面比以往的方法提高了23%至52%。我们认为这项工作不仅推动了语言身份识别的研究进展，还为代码转换语音技术建立了基础框架。
URL
https://arxiv.org/abs/2510.00582
