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        <title>Fine-Tuning ASR models for Very Low-Resource Languages: A Study on Mvskoke</title>
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            <namePart type="given">Xiyan</namePart>
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    <abstract>Recent advancements in multilingual models for automatic speech recognition (ASR) have been able to achieve a high accuracy for languages with extremely limited resources. This study examines ASR modeling for the Mvskoke language, an indigenous language of America. The parameter efficiency of adapter training is contrasted with training entire models, and it is demonstrated how performance varies with different amounts of data. Additionally, the models are evaluated with trigram language model decoding, and the outputs are compared across different types of speech recordings. Results show that training an adapter is both parameter efficient and gives higher accuracy for a relatively small amount of data.</abstract>
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    <identifier type="doi">10.18653/v1/2024.acl-srw.16</identifier>
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%0 Conference Proceedings
%T Fine-Tuning ASR models for Very Low-Resource Languages: A Study on Mvskoke
%A Mainzinger, Julia
%A Levow, Gina-Anne
%Y Fu, Xiyan
%Y Fleisig, Eve
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F mainzinger-levow-2024-fine
%X Recent advancements in multilingual models for automatic speech recognition (ASR) have been able to achieve a high accuracy for languages with extremely limited resources. This study examines ASR modeling for the Mvskoke language, an indigenous language of America. The parameter efficiency of adapter training is contrasted with training entire models, and it is demonstrated how performance varies with different amounts of data. Additionally, the models are evaluated with trigram language model decoding, and the outputs are compared across different types of speech recordings. Results show that training an adapter is both parameter efficient and gives higher accuracy for a relatively small amount of data.
%R 10.18653/v1/2024.acl-srw.16
%U https://aclanthology.org/2024.acl-srw.16
%U https://doi.org/10.18653/v1/2024.acl-srw.16
%P 76-82

Markdown (Informal)

[Fine-Tuning ASR models for Very Low-Resource Languages: A Study on Mvskoke](https://aclanthology.org/2024.acl-srw.16) (Mainzinger & Levow, ACL 2024)

• Fine-Tuning ASR models for Very Low-Resource Languages: A Study on Mvskoke (Mainzinger & Levow, ACL 2024)

ACL

• Julia Mainzinger and Gina-Anne Levow. 2024. Fine-Tuning ASR models for Very Low-Resource Languages: A Study on Mvskoke. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 76–82, Bangkok, Thailand. Association for Computational Linguistics.