  Automatic speech recognition (ASR) on low resource languages improves the
access of linguistic minorities to technological advantages provided by
artificial intelligence (AI). In this paper, we address the problem of data
scarcity for the Hong Kong Cantonese language by creating a new Cantonese
dataset. Our dataset, Multi-Domain Cantonese Corpus (MDCC), consists of 73.6
hours of clean read speech paired with transcripts, collected from Cantonese
audiobooks from Hong Kong. It comprises philosophy, politics, education,
culture, lifestyle and family domains, covering a wide range of topics. We also
review all existing Cantonese datasets and analyze them according to their
speech type, data source, total size and availability. We further conduct
experiments with Fairseq S2T Transformer, a state-of-the-art ASR model, on the
biggest existing dataset, Common Voice zh-HK, and our proposed MDCC, and the
results show the effectiveness of our dataset. In addition, we create a
powerful and robust Cantonese ASR model by applying multi-dataset learning on
MDCC and Common Voice zh-HK.
