VietMed: A Dataset and Benchmark for Automatic Speech Recognition of Vietnamese in the Medical Domain

Description:

We introduced a Vietnamese speech recognition dataset in the medical domain comprising 16h of labeled medical speech, 1000h of unlabeled medical speech and 1200h of unlabeled general-domain speech. To our best knowledge, VietMed is by far the world’s largest public medical speech recognition dataset in 7 aspects: total duration, number of speakers, diseases, recording conditions, speaker roles, unique medical terms and accents. VietMed is also by far the largest public Vietnamese speech dataset in terms of total duration. Additionally, we are the first to present a medical ASR dataset covering all ICD-10 disease groups and all accents within a country.

Please cite this paper: https://arxiv.org/abs/2404.05659

@inproceedings{VietMed_dataset,
  title={VietMed: A Dataset and Benchmark for Automatic Speech Recognition of Vietnamese in the Medical Domain}, 
  author={Khai Le-Duc},
  year={2024},
  booktitle = {Proceedings of the Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
}

For labeled data, please refer to our HuggingFace, Paperswithcodes.

For full dataset (labeled data + unlabeled data), please refer to Google Drive

Contact:

If any links are broken, please contact me for fixing!

Le Duc Khai
University of Toronto, Canada
Email: duckhai.le@mail.utoronto.ca
GitHub: https://github.com/leduckhai