Methods to Increase the Amount of Data for Speech Recognition for Low Resource Languages
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915141957713920 |
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| author | Ayrapetyan, Alexan Kostandian, Sofia Yeroyan, Ara Yerznkanyan, Mher Karpov, Nikolay Tadevosyan, Nune Lavrukhin, Vitaly Ginsburg, Boris |
| author_facet | Ayrapetyan, Alexan Kostandian, Sofia Yeroyan, Ara Yerznkanyan, Mher Karpov, Nikolay Tadevosyan, Nune Lavrukhin, Vitaly Ginsburg, Boris |
| contents | This study explores methods to increase data volume for low-resource languages using techniques such as crowdsourcing, pseudo-labeling, advanced data preprocessing and various permissive data sources such as audiobooks, Common Voice, YouTube. While these methods are well-explored for highresource languages, their application for low-resource languages remains underexplored. Using Armenian and Georgian as case studies, we demonstrate how linguistic and resource-specific characteristics influence the success of these methods. This work provides practical guidance for researchers to choose cost-effective and quality-driven dataset extension strategies for low-resource languages. The key takeaway from various data extension approaches is that paid crowd-sourcing offers the best balance between cost and quality, outperforming volunteer crowd-sourcing, open-source audiobooks, and unlabeled data usage. Ablation study shows that models trained on the expanded datasets outperform existing baselines and achieve 5.73% for Gergian and 9.9% for Armenian ASR word error rate using a relatively small FastConformer architecture. We open-sourced both the Armenian and Georgian models to allow further research and practical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14788 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Methods to Increase the Amount of Data for Speech Recognition for Low Resource Languages Ayrapetyan, Alexan Kostandian, Sofia Yeroyan, Ara Yerznkanyan, Mher Karpov, Nikolay Tadevosyan, Nune Lavrukhin, Vitaly Ginsburg, Boris Sound Computation and Language Audio and Speech Processing This study explores methods to increase data volume for low-resource languages using techniques such as crowdsourcing, pseudo-labeling, advanced data preprocessing and various permissive data sources such as audiobooks, Common Voice, YouTube. While these methods are well-explored for highresource languages, their application for low-resource languages remains underexplored. Using Armenian and Georgian as case studies, we demonstrate how linguistic and resource-specific characteristics influence the success of these methods. This work provides practical guidance for researchers to choose cost-effective and quality-driven dataset extension strategies for low-resource languages. The key takeaway from various data extension approaches is that paid crowd-sourcing offers the best balance between cost and quality, outperforming volunteer crowd-sourcing, open-source audiobooks, and unlabeled data usage. Ablation study shows that models trained on the expanded datasets outperform existing baselines and achieve 5.73% for Gergian and 9.9% for Armenian ASR word error rate using a relatively small FastConformer architecture. We open-sourced both the Armenian and Georgian models to allow further research and practical applications. |
| title | Methods to Increase the Amount of Data for Speech Recognition for Low Resource Languages |
| topic | Sound Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2501.14788 |