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--- |
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license: apache-2.0 |
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--- |
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# ManWav |
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[ManWav: The First Manchu ASR Model](https://arxiv.org/pdf/2406.13502) is the fine-tuned Wav2Vec2-XLSR-53 model with Manchu audio data. |
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## Data |
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[Link](https://github.com/seemdog/ManWav) to Manchu audio data |
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## Citation |
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``` |
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@inproceedings{seo-etal-2024-manwav, |
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title = "{M}an{W}av: The First {M}anchu {ASR} Model", |
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author = "Seo, Jean and |
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Kang, Minha and |
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Byun, SungJoo and |
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Lee, Sangah", |
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editor = "Serikov, Oleg and |
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Voloshina, Ekaterina and |
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Postnikova, Anna and |
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Muradoglu, Saliha and |
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Le Ferrand, Eric and |
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Klyachko, Elena and |
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Vylomova, Ekaterina and |
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Shavrina, Tatiana and |
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Tyers, Francis", |
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booktitle = "Proceedings of the 3rd Workshop on NLP Applications to Field Linguistics (Field Matters 2024)", |
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month = aug, |
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year = "2024", |
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address = "Bangkok, Thailand", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2024.fieldmatters-1.2", |
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pages = "6--11", |
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abstract = "This study addresses the widening gap in Automatic Speech Recognition (ASR) research between high resource and extremely low resource languages, with a particular focus on Manchu, a severely endangered language. Manchu exemplifies the challenges faced by marginalized linguistic communities in accessing state-of-the-art technologies. In a pioneering effort, we introduce the first-ever Manchu ASR model ManWav, leveraging Wav2Vec2-XLSR-53. The results of the first Manchu ASR is promising, especially when trained with our augmented data. Wav2Vec2-XLSR-53 fine-tuned with augmented data demonstrates a 0.02 drop in CER and 0.13 drop in WER compared to the same base model fine-tuned with original data.", |
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} |
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``` |
