SwitchLingua: The First Large-Scale Multilingual and Multi-Ethnic Code-Switching Dataset

• URL: http://arxiv.org/abs/2506.00087v1
• Date: Fri, 30 May 2025 05:54:46 GMT
• Title: SwitchLingua: The First Large-Scale Multilingual and Multi-Ethnic Code-Switching Dataset
• Authors: Peng Xie, Xingyuan Liu, Tsz Wai Chan, Yequan Bie, Yangqiu Song, Yang Wang, Hao Chen, Kani Chen,
• Abstract summary: Code-Switching (CS) is the alternating use of two or more languages within a conversation or utterance.<n>This linguistic phenomenon poses challenges for Automatic Speech Recognition (ASR) systems.<n>textbfSwitchLingua is the first large-scale multilingual and multi-ethnic code-switching dataset.
• Score: 34.40254709148148
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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