mlsanityData to 7 Oct 2026

Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset

arXiv 2201.02419 ↗First posted 7 Jan 2022cs.CL cs.SD eess.AScited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 20 of its 37 references that are in the map. First posted on arXiv 7 Jan 2022.

Builds on

20 of 37 references.

References · 20

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  11. 2020Self-training and Pre-training are Complementary for Speech Recognitioncited by 98
  12. 2019Lightweight and Efficient End-to-End Speech Recognition Using Low-Rank Transformercited by 47
8 more
  1. 2019Transformers with convolutional context for ASRcited by 44
  2. 2021ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversationcited by 43
  3. 2020Adapt-and-Adjust: Overcoming the Long-Tail Problem of Multilingual Speech Recognitioncited by 34
  4. 2020Learning Fast Adaptation on Cross-Accented Speech Recognitioncited by 28
  5. 2020Meta-Transfer Learning for Code-Switched Speech Recognitioncited by 22
  6. 2020Improved End-to-End Dysarthric Speech Recognition via Meta-learning Based Model Re-initializationcited by 17
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 2; 2025: 4; 2026: 2.

12 papers.

  1. 2025SwitchLingua: The First Large-Scale Multilingual and Multi-Ethnic Code-Switching Datasetcited by 12
  2. 2025The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varietiescited by 4
  3. 2023HK-LegiCoST: Leveraging Non-Verbatim Transcripts for Speech Translationcited by 3
  4. 2023Developing a Multilingual Dataset and Evaluation Metrics for Code-Switching: A Focus on Hong Kong's Polylingual Dynamicscited by 3
  5. 2025CantoASR: Prosody-Aware ASR-LALM Collaboration for Low-Resource Cantonesecited by 3
  6. 2024TouchASP: Elastic Automatic Speech Perception that Everyone Can Touchcited by 2
  7. 2023Improving Rare Words Recognition through Homophone Extension and Unified Writing for Low-resource Cantonese Speech Recognitioncited by 1
  8. 2025Developing and Utilizing a Large-Scale Cantonese Dataset for Multi-Tasking in Large Language Modelscited by 1
  9. 2026Toward a Cross-Lingual Romanization Ecosystem for Sinitic Languages: A Paired Mandarin-Cantonese Case Studycited by 1
  10. 2022Kaggle Competition: Cantonese Audio-Visual Speech Recognition for In-car Commandscited by 0
  11. 2024Transliterated Zero-Shot Domain Adaptation for Automatic Speech Recognitioncited by 0
  12. 2026Towards Comprehensive Semantic Speech Embeddings for Chinese Dialectscited by 0