English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

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