SwitchLingua：首个大规模多语言与多民族语码转换数据集
SwitchLingua: The First Large-Scale Multilingual and Multi-Ethnic Code-Switching Dataset
AI总结：
本文提出多智能体框架LinguaMaster构建了首个大规模多语言多民族语码转换数据集SwitchLingua，并提出了结合语义信息的评估指标SAER以更准确评估ASR系统。
摘要：
语码转换（CS）是指在对话或话语中交替使用两种或多种语言，通常受社会语境和说话者身份的影响。这种语言现象给自动语音识别（ASR）系统带来了挑战，这些系统通常为单一语言设计，难以处理多语言输入。包括语码转换ASR（CSASR）、文本转语音（CSTTS）和跨语言信息检索（CLIR）在内的多语言应用在全球日益增长的需求，凸显了现有单语数据集的不足。尽管存在一些语码转换数据集，但大多数局限于同质族群内的双语混合，迫切需要一个类似于计算机视觉中ImageNet的大规模、多样化基准。为了弥补这一空白，我们引入了LinguaMaster，这是一个专门为高效和可扩展的多语言数据合成而设计的多智能体协作框架。利用该框架，我们构建了SwitchLingua，这是首个大规模多语言和多民族语码转换数据集，包括：(1) 涵盖12种语言的420K条CS文本样本，以及(2) 基于该文本数据，来自代表18个国家/地区和63个种族/民族背景的174名说话者的超过80小时的录音。该数据集捕捉了丰富的语言和文化多样性，为推进多语言和多文化研究提供了基础资源。此外，为了解决现有ASR评估指标对语码转换场景缺乏敏感性的问题，我们提出了语义感知错误率（SAER），这是一种结合了语义信息的新颖评估指标，为系统性能提供了更准确和上下文感知的评估。
英文摘要：
Code-switching (CS) is the alternating use of two or more languages within a conversation or utterance, often influenced by social context and speaker identity. This linguistic phenomenon poses challenges for Automatic Speech Recognition (ASR) systems, which are typically designed for a single language and struggle to handle multilingual inputs. The growing global demand for multilingual applications, including Code-Switching ASR (CSASR), Text-to-Speech (CSTTS), and Cross-Lingual Information Retrieval (CLIR), highlights the inadequacy of existing monolingual datasets. Although some code-switching datasets exist, most are limited to bilingual mixing within homogeneous ethnic groups, leaving a critical need for a large-scale, diverse benchmark akin to ImageNet in computer vision. To bridge this gap, we introduce \textbf{LinguaMaster}, a multi-agent collaboration framework specifically designed for efficient and scalable multilingual data synthesis. Leveraging this framework, we curate \textbf{SwitchLingua}, the first large-scale multilingual and multi-ethnic code-switching dataset, including: (1) 420K CS textual samples across 12 languages, and (2) over 80 hours of audio recordings from 174 speakers representing 18 countries/regions and 63 racial/ethnic backgrounds, based on the textual data. This dataset captures rich linguistic and cultural diversity, offering a foundational resource for advancing multilingual and multicultural research. Furthermore, to address the issue that existing ASR evaluation metrics lack sensitivity to code-switching scenarios, we propose the \textbf{Semantic-Aware Error Rate (SAER)}, a novel evaluation metric that incorporates semantic information, providing a more accurate and context-aware assessment of system performance.
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。
