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viewer: true |
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# VietMed: A Dataset and Benchmark for Automatic Speech Recognition of Vietnamese in the Medical Domain |
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## Description: |
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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. |
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To our best knowledge, VietMed is by far **the world’s largest public medical speech recognition dataset** in 7 aspects: |
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total duration, number of speakers, diseases, recording conditions, speaker roles, unique medical terms and accents. |
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VietMed is also by far the largest public Vietnamese speech dataset in terms of total duration. |
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Additionally, we are the first to present a medical ASR dataset covering all ICD-10 disease groups and all accents within a country. |
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Please cite this paper: https://arxiv.org/abs/2404.05659 |
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@inproceedings{VietMed_dataset, |
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title={VietMed: A Dataset and Benchmark for Automatic Speech Recognition of Vietnamese in the Medical Domain}, |
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author={Khai Le-Duc}, |
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year={2024}, |
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booktitle = {Proceedings of the Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)}, |
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} |
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To load labeled data, please refer to our [HuggingFace](https://huggingface.co./datasets/leduckhai/VietMed), [Paperswithcodes](https://paperswithcode.com/dataset/vietmed). |
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For full dataset (labeled data + unlabeled data) and pre-trained models, please refer to [Google Drive](https://drive.google.com/drive/folders/1hsoB_xjWh66glKg3tQaSLm4S1SVPyANP?usp=sharing) |
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## Limitations: |
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Since this dataset is human-labeled, 1-2 ending/starting words present in the recording might not be present in the transcript. |
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That's the nature of human-labeled dataset, in which humans can't distinguish words that are faster than 1 second. |
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In contrast, forced alignment could solve this problem because machines can "listen" words in 10ms-20ms. |
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However, forced alignment only learns what it is taught by humans. |
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Therefore, no transcript is perfect. We will conduct human-machine collaboration to get "more perfect" transcript in the next paper. |
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## Contact: |
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If any links are broken, please contact me for fixing! |
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Thanks [Phan Phuc](https://www.linkedin.com/in/pphuc/) for dataset viewer <3 |
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``` |
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Le Duc Khai |
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University of Toronto, Canada |
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Email: [email protected] |
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GitHub: https://github.com/leduckhai |
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