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+# MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder
+
+## Description:
+Multilingual automatic speech recognition (ASR) in the medical domain serves as a foundational task for various downstream applications such as speech translation, spoken language understanding, and voice-activated assistants. 
+This technology enhances patient care by enabling efficient communication across language barriers, alleviating specialized workforce shortages, and facilitating improved diagnosis and treatment, particularly during pandemics. 
+In this work, we introduce *MultiMed*, a collection of small-to-large end-to-end ASR models for the medical domain, spanning five languages: Vietnamese, English, German, French, and Mandarin Chinese, together with the corresponding real-world ASR dataset. 
+To our best knowledge, *MultiMed* stands as **the largest and the first multilingual medical ASR dataset**, in terms of total duration, number of speakers, diversity of diseases, recording conditions, speaker roles, unique medical terms, accents, and ICD-10 codes. 
+
+
+Please cite this paper: [https://arxiv.org/abs/2409.14074](https://arxiv.org/abs/2409.14074)
+
+    @inproceedings{le2024multimed,
+      title={MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder},
+      author={Le-Duc, Khai and Phan, Phuc and Pham, Tan-Hanh and Tat, Bach Phan and Ngo, Minh-Huong and Hy, Truong-Son},
+      journal={arXiv preprint arXiv:2409.14074},
+      year={2024}
+    }
+To load labeled data, please refer to our [HuggingFace](https://huggingface.co/datasets/leduckhai/MultiMed), [Paperswithcodes](https://paperswithcode.com/dataset/multimed).
+
+## Contact:
+
+If any links are broken, please contact me for fixing!
+
+```
+Le Duc Khai
+University of Toronto, Canada
+Email: [email protected]
+GitHub: https://github.com/leduckhai
+```