Thailand

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.

ACL 2024

Fine-Tuning ASR models for Very Low-Resource Languages: A Study on Mvskoke

endangered languages

low-resource languages

speech

poster

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

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Fine-Tuning ASR models for Very Low-Resource Languages: A Study on Mvskoke

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Label-Aware Automatic Verbalizer for Few-Shot Text Classification in Mid-To-Low Resource Languages

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