# SwitchLingua: The First Large-Scale Multilingual and Multi-Ethnic Code-Switching Dataset arXiv: https://arxiv.org/abs/2506.00087 ยท HTML: https://mlsanity.com/paper/2506.00087/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 20 of its 50 references that are in the map. - First posted on arXiv 30 May 2025. ## Builds on (most cited first, 20 of 20) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022) - [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020) - [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023) - [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023) - [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023) - [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020) - [Qwen2-Audio Technical Report](https://mlsanity.com/paper/2407.10759/) (2024) - [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020) - [Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents](https://mlsanity.com/paper/2306.03314/) (2023) - [Seamless: Multilingual Expressive and Streaming Speech Translation](https://mlsanity.com/paper/2312.05187/) (2023) - [FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMs](https://mlsanity.com/paper/2407.04051/) (2024) - [ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation](https://mlsanity.com/paper/2112.06223/) (2021) - [Chain of Attack: On the Robustness of Vision-Language Models Against Transfer-Based Adversarial Attacks](https://mlsanity.com/paper/2411.15720/) (2024) - [Advocating Character Error Rate for Multilingual ASR Evaluation](https://mlsanity.com/paper/2410.07400/) (2024) - [End-to-End Code-Switching ASR for Low-Resourced Language Pairs](https://mlsanity.com/paper/1909.12681/) (2019) - [Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset](https://mlsanity.com/paper/2201.02419/) (2022) - [Code-Switching Text Generation and Injection in Mandarin-English ASR](https://mlsanity.com/paper/2303.10949/) (2023) - [Linguistics Theory Meets LLM: Code-Switched Text Generation via Equivalence Constrained Large Language Models](https://mlsanity.com/paper/2410.22660/) (2024) - [Developing a Multilingual Dataset and Evaluation Metrics for Code-Switching: A Focus on Hong Kong's Polylingual Dynamics](https://mlsanity.com/paper/2310.17953/) (2023) ## Built on by (most cited first, 14 of 14) - [Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models across Modalities](https://mlsanity.com/paper/2510.07037/) (2025) - [Bringing Up a Bilingual BabyLM: Investigating Multilingual Language Acquisition Using Small-Scale Models](https://mlsanity.com/paper/2603.29552/) (2026) - [Gradual Code-Switching as Inference-Time Cross-Lingual Representational Alignment for LLMs](https://mlsanity.com/paper/2510.05678/) (2025) - [OLA: Output Language Alignment in Code-Switched LLM Interactions](https://mlsanity.com/paper/2601.03589/) (2026) - [Towards Fine-Grained Code-Switch Speech Translation with Semantic Space Alignment](https://mlsanity.com/paper/2511.10670/) (2025) - [Benchmarking Commercial ASR Systems on Code-Switching Speech: Arabic, Persian, and German](https://mlsanity.com/paper/2605.19069/) (2026) - [Phrase-Localized Language-Contrastive Guidance: Training-Free Localized Accent Control for Code-Switching Text-to-Speech](https://mlsanity.com/paper/2609.01016/) (2026) - [Back to Basics: Revisiting ASR in the Age of Voice Agents](https://mlsanity.com/paper/2603.25727/) (2026) - [MUSCAT: MUltilingual, SCientific ConversATion Benchmark](https://mlsanity.com/paper/2604.15929/) (2026) - [Reinforcement Learning for Data-Efficient Code-Switched ASR](https://mlsanity.com/paper/2607.02757/) (2026) - [AfriSwitch: A Benchmark for In-the-Wild African Code-Switched Speech Recognition](https://mlsanity.com/paper/2608.26434/) (2026) - [Domain-specific Pretraining Profile and Transformer Performance: Evidence from Modeling Digital Pragmatics in Arabic-English Code-switching](https://mlsanity.com/paper/2609.14571/) (2026) - [CoSE-E: A Benchmark for Code-switched Speech Evaluation in Enterprise Settings](https://mlsanity.com/paper/2609.35645/) (2026) - [CSWBench: Evaluating Context-Sensitive Code-Switching in Creative Writing](https://mlsanity.com/paper/2610.20272/) (2026) ## Abstract (arXiv) 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 LinguaMaster, a multi-agent collaboration framework specifically designed for efficient and scalable multilingual data synthesis. Leveraging this framework, we curate 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 Semantic-Aware Error Rate (SAER), a novel evaluation metric that incorporates semantic information, providing a more accurate and context-aware assessment of system performance.