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    <abstract>We introduce BuzzASR, a collection of language-specialized fine-tuned Whisper models adapted for automatic speech recognition (ASR) in 102 languages. Large end-to-end Transformer-based ASR models such as Whisper have revolutionized ASR, but most prominent models are highly multilingual. As a result, these models often perform poorly on languages less well-represented in their training set. While it has long been known that effective language adaptation can be achieved through simple fine-tuning on monolingual data, this strategy has only been applied to a small number of languages. We massively scale up this simple approach to 102 languages covered in the FLEURS dataset, while also implementing a more complex language adaptation strategy that integrates monolingual tokenizer replacement and data augmentation using text-only fine-tuning. BuzzASR models outperform Whisper on 77 out of 102 languages, reducing character error rates (CER) by a factor of over 2.8 on average. Our models achieve state-of-the-art CER among open-source systems on 27 of 102 languages on the combined FLEURS and Common Voice test set. Our tokenizer replacement strategy yields an average 3.4x improvement in compression rate (characters per token) over Whisper’s multilingual BPE, with gains of up to 21.7x.</abstract>
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%0 Conference Proceedings
%T BuzzASR: A Swarm of 100+ Monolingual Speech Recognition Models
%A Singh, Shivam
%A Yadavalli, Aditya
%A Arnett, Catherine
%A Warstadt, Alex
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F singh-etal-2026-buzzasr
%X We introduce BuzzASR, a collection of language-specialized fine-tuned Whisper models adapted for automatic speech recognition (ASR) in 102 languages. Large end-to-end Transformer-based ASR models such as Whisper have revolutionized ASR, but most prominent models are highly multilingual. As a result, these models often perform poorly on languages less well-represented in their training set. While it has long been known that effective language adaptation can be achieved through simple fine-tuning on monolingual data, this strategy has only been applied to a small number of languages. We massively scale up this simple approach to 102 languages covered in the FLEURS dataset, while also implementing a more complex language adaptation strategy that integrates monolingual tokenizer replacement and data augmentation using text-only fine-tuning. BuzzASR models outperform Whisper on 77 out of 102 languages, reducing character error rates (CER) by a factor of over 2.8 on average. Our models achieve state-of-the-art CER among open-source systems on 27 of 102 languages on the combined FLEURS and Common Voice test set. Our tokenizer replacement strategy yields an average 3.4x improvement in compression rate (characters per token) over Whisper’s multilingual BPE, with gains of up to 21.7x.
%U https://aclanthology.org/2026.findings-emnlp.2335/
%P 50199-50227

Markdown (Informal)

[BuzzASR: A Swarm of 100+ Monolingual Speech Recognition Models](https://aclanthology.org/2026.findings-emnlp.2335/) (Singh et al., Findings 2026)

• BuzzASR: A Swarm of 100+ Monolingual Speech Recognition Models (Singh et al., Findings 2026)

ACL

• Shivam Singh, Aditya Yadavalli, Catherine Arnett, and Alex Warstadt. 2026. BuzzASR: A Swarm of 100+ Monolingual Speech Recognition Models. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 50199–50227, Budapest, Hungary. Association for Computational Linguistics.