CS-FLEURS: A Massively Multilingual and Code-Switched Speech Dataset

• URL: http://arxiv.org/abs/2509.14161v1
• Date: Wed, 17 Sep 2025 16:45:22 GMT
• Title: CS-FLEURS: A Massively Multilingual and Code-Switched Speech Dataset
• Authors: Brian Yan, Injy Hamed, Shuichiro Shimizu, Vasista Lodagala, William Chen, Olga Iakovenko, Bashar Talafha, Amir Hussein, Alexander Polok, Kalvin Chang, Dominik Klement, Sara Althubaiti, Puyuan Peng, Matthew Wiesner, Thamar Solorio, Ahmed Ali, Sanjeev Khudanpur, Shinji Watanabe, Chih-Chen Chen, Zhen Wu, Karim Benharrak, Anuj Diwan, Samuele Cornell, Eunjung Yeo, Kwanghee Choi, Carlos Carvalho, Karen Rosero,
• Abstract summary: CS-FLEURS consists of 4 test sets which cover in total 113 unique code-switched language pairs across 52 languages.<n> CS-FLEURS also provides a training set with 128 hours of generative text-to-speech data across 16 X-English language pairs.
• Score: 99.0507412649934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present CS-FLEURS, a new dataset for developing and evaluating code-switched speech recognition and translation systems beyond high-resourced languages. CS-FLEURS consists of 4 test sets which cover in total 113 unique code-switched language pairs across 52 languages: 1) a 14 X-English language pair set with real voices reading synthetically generated code-switched sentences, 2) a 16 X-English language pair set with generative text-to-speech 3) a 60 {Arabic, Mandarin, Hindi, Spanish}-X language pair set with the generative text-to-speech, and 4) a 45 X-English lower-resourced language pair test set with concatenative text-to-speech. Besides the four test sets, CS-FLEURS also provides a training set with 128 hours of generative text-to-speech data across 16 X-English language pairs. Our hope is that CS-FLEURS helps to broaden the scope of future code-switched speech research. Dataset link: https://huggingface.co/datasets/byan/cs-fleurs.

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