Low-Resource Speech Recognition Based on Transfer Learning

Wei Hong Tsai, Phuong Le Thi, Tzu Chiang Tai, Chien Lin Huang, Jia Ching Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

A lot of research aims to improve accuracy in end-to-end speech recognition, and achieves higher accuracy on various famous corpora. However, there are many languages which do not have enough data to build their speech recognition system in the world. The system often cannot get a reliable result and be used in the real-world. Therefore, how to build a robust low-resource speech recognition system is an important issue in speech recognition. In this paper, we use ESPnet toolkit to implement an end-to-end speech recognition model based on sequence-to-sequence architecture, and use Fairseq toolkit to implement an unsupervised pre-training model for assisted speech recognition. In addition, we use unlabeled speech data to help extract speech features, and transfer a speech recognition model with sufficient corpus to Hakka speech recognition with less corpus through transfer learning. Experimental results show that we establish a more robust low-resource Hakka speech recognition system.

Original languageEnglish
Title of host publicationProceedings - 2022 RIVF International Conference on Computing and Communication Technologies, RIVF 2022
EditorsVo Nguyen Quoc Bao, Tran Manh Ha
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages145-149
Number of pages5
ISBN (Electronic)9781665461665
DOIs
StatePublished - 2022
Event2022 RIVF International Conference on Computing and Communication Technologies, RIVF 2022 - Ho Chi Minh City, Viet Nam
Duration: 20 Dec 202222 Dec 2022

Publication series

NameProceedings - 2022 RIVF International Conference on Computing and Communication Technologies, RIVF 2022

Conference

Conference2022 RIVF International Conference on Computing and Communication Technologies, RIVF 2022
Country/TerritoryViet Nam
CityHo Chi Minh City
Period20/12/2222/12/22

Keywords

  • computational paralinguistics
  • human-computer interaction
  • speech recognition

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