MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder
<|reference_start|>MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder: 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. Secondly, we establish the empirical baselines, present the first reproducible study of multilinguality in medical ASR, conduct a layer-wise ablation study for end-to-end ASR training, and provide the first linguistic analysis for multilingual medical ASR. All code, data, and models are available online https://github.com/leduckhai/MultiMed/tree/master/MultiMed<|reference_end|>
@article{le-duc2024multimed:,
    title={MultiMed: Multilingual Medical Speech Recognition via Attention Encoder
  Decoder},
    author={Khai Le-Duc, Phuc Phan, Tan-Hanh Pham, Bach Phan Tat, Minh-Huong Ngo,
  Truong-Son Hy},
    journal={arXiv preprint arXiv:2409.14074},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14074},
    primaryClass={cs.CL cs.SD eess.AS}
}