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

Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), pages 6487–6494 Marseille, 20-25 June 2022 © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0 6487 Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset Tiezheng Yu★, Rita Frieske★, Peng Xu★ †, Samuel Cahyawijaya★, Cheuk Tung Shadow Yiu, Holy Lovenia, Wenliang Dai, Elham J. Barezi Qifeng Chen, Xiaojuan Ma, Bertram E. Shi, Pascale Fung The Hong Kong University of Science and Technology {tyuah, peng.xu, scahyawijaya}@connect.ust.hk rita.frieske@ust.hk Abstract 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. Keywords: Speech Corpus, Hong Kong Cantonese, Automatic Speech Recognition System 1. Introduction Automatic speech recognition (ASR) systems take the audio as input and convert it into text (Malik et al., 2021). Due to the popularization of deep learning, ASR technology has grown rapidly and has led to a signifi- cant improvement in recognizing many languages. For instance, ASR systems in English (Zhang et al., 2020; Xu et al., 2021; Baevski et al., 2020) have been able to achieve a below 2% word error rate (WER) on the Lib- riSpeech (Panayotov et al., 2015) corpus. A similar trend is also observed in research on Chinese ASR (Li et al., 2019; Winata et al., 2020a; Zhang et al., 2020), exempli- fied by the improvement in the ASR model performance on the Aishell-1 (Bu et al., 2017) corpus from 18.7% character error rate (CER) down to 6.84% within just two years. However, many languages (e.g., Gujarati, Hindi, Bengali, Amharic and Cantonese), including those that feature code-switching, are still lacking resources, and the performance of ASR systems in these languages is unsatisfactory (Winata et al., 2021; Khare et al., 2021; Lovenia et al., 2021). Therefore, many methods in ASR have also been introduced and have shown promising results (Wang et al., 2021; Lin and Chen, 2020; Winata et al., 2020b; Winata et al., 2020c). Many of these achievements are due to the utilization of the most recent deep neural network architectures and high-performance parallel computing graphics cards. However, since deep learning techniques require large amounts of training data, the creation of ASR datasets is …

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