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

arXiv: https://arxiv.org/abs/2201.02419 · HTML: https://mlsanity.com/paper/2201.02419/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 7 Jan 2022.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition](https://mlsanity.com/paper/2010.10504/) (2020)
- [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://mlsanity.com/paper/2010.05171/) (2020)
- [Self-training and Pre-training are Complementary for Speech Recognition](https://mlsanity.com/paper/2010.11430/) (2020)
- [Lightweight and Efficient End-to-End Speech Recognition Using Low-Rank Transformer](https://mlsanity.com/paper/1910.13923/) (2019)
- [Transformers with convolutional context for ASR](https://mlsanity.com/paper/1904.11660/) (2019)
- [ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation](https://mlsanity.com/paper/2112.06223/) (2021)
- [Adapt-and-Adjust: Overcoming the Long-Tail Problem of Multilingual Speech Recognition](https://mlsanity.com/paper/2012.01687/) (2020)
- [Learning Fast Adaptation on Cross-Accented Speech Recognition](https://mlsanity.com/paper/2003.01901/) (2020)
- [Meta-Transfer Learning for Code-Switched Speech Recognition](https://mlsanity.com/paper/2004.14228/) (2020)
- [Improved End-to-End Dysarthric Speech Recognition via Meta-learning Based Model Re-initialization](https://mlsanity.com/paper/2011.01686/) (2020)
- [Zipf's law in 50 languages: its structural pattern, linguistic interpretation, and cognitive motivation](https://mlsanity.com/paper/1807.01855/) (2018)
- [End-to-end Speech Recognition with Adaptive Computation Steps](https://mlsanity.com/paper/1808.10088/) (2018)

## Built on by (most cited first, 12 of 12)

- [SwitchLingua: The First Large-Scale Multilingual and Multi-Ethnic Code-Switching Dataset](https://mlsanity.com/paper/2506.00087/) (2025)
- [The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties](https://mlsanity.com/paper/2509.07139/) (2025)
- [HK-LegiCoST: Leveraging Non-Verbatim Transcripts for Speech Translation](https://mlsanity.com/paper/2306.11252/) (2023)
- [Developing a Multilingual Dataset and Evaluation Metrics for Code-Switching: A Focus on Hong Kong's Polylingual Dynamics](https://mlsanity.com/paper/2310.17953/) (2023)
- [TouchASP: Elastic Automatic Speech Perception that Everyone Can Touch](https://mlsanity.com/paper/2412.15622/) (2024)
- [CantoASR: Prosody-Aware ASR-LALM Collaboration for Low-Resource Cantonese](https://mlsanity.com/paper/2511.04139/) (2025)
- [Improving Rare Words Recognition through Homophone Extension and Unified Writing for Low-resource Cantonese Speech Recognition](https://mlsanity.com/paper/2302.00836/) (2023)
- [Developing and Utilizing a Large-Scale Cantonese Dataset for Multi-Tasking in Large Language Models](https://mlsanity.com/paper/2503.03702/) (2025)
- [Towards Comprehensive Semantic Speech Embeddings for Chinese Dialects](https://mlsanity.com/paper/2601.07274/) (2026)
- [Toward a Cross-Lingual Romanization Ecosystem for Sinitic Languages: A Paired Mandarin-Cantonese Case Study](https://mlsanity.com/paper/2608.29170/) (2026)
- [Kaggle Competition: Cantonese Audio-Visual Speech Recognition for In-car Commands](https://mlsanity.com/paper/2207.02663/) (2022)
- [Transliterated Zero-Shot Domain Adaptation for Automatic Speech Recognition](https://mlsanity.com/paper/2412.11185/) (2024)

## 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.