Vietnamese Automatic Speech Recognition: A Revisit
Open accessAbstract
Automatic Speech Recognition (ASR) performance is heavily dependent on the availability of large-scale, high-quality datasets.For low-resource languages, existing open-source ASR datasets often suffer from insufficient quality and inconsistent annotation, hindering the development of robust models.To address these challenges, we propose a novel and generalizable data aggregation and preprocessing pipeline designed to construct high-quality ASR datasets from diverse, potentially noisy, open-source sources.Our pipeline incorporates rigorous processing steps to ensure data diversity, balance, and the inclusion of crucial features like word-level timestamps.We demonstrate the effectiveness of our methodology by applying it to Vietnamese, resulting in a unified, high-quality 500-hour dataset that provides a foundation for training and evaluating state-of-the-art Vietnamese ASR systems.Our project page is available
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- Authors
- Vu, T., Nguyen, L. T., & Nguyen, D. Q.
- Affiliations
- Qualcomm (United States) (US)
- Venue
- Findings of the Association for Computational Linguistics: EACL 2026
- Published
- 2026
- Pages
- 6557-6568
- OpenAlex
- W7140116537
- Cited by
- 0
- References
- 0
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: EACL (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- Vietnamese speech recognition, low-resource languages, data aggregation, data preprocessing, data quality, annotation consistency
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