VietMed: A Dataset and Benchmark for Automatic Speech Recognition of
  Vietnamese in the Medical Domain
Khai Le-Duc
Due to privacy restrictions, there's a shortage of publicly available speech
recognition datasets in the medical domain. In this work, we present VietMed -
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. Moreover, we release the first public large-scale
pre-trained models for Vietnamese ASR, w2v2-Viet and XLSR-53-Viet, along with
the first public large-scale fine-tuned models for medical ASR. Even without
any medical data in unsupervised pre-training, our best pre-trained model
XLSR-53-Viet generalizes very well to the medical domain by outperforming
state-of-the-art XLSR-53, from 51.8% to 29.6% WER on test set (a relative
reduction of more than 40%). All code, data and models are made publicly
available here: https://github.com/leduckhai/MultiMed.