MUST: A MUltilingual Student-Teacher learning approach for low-resource speech recognition

This is the latest version of this eprint.

Farooq, M.U., Ahmad, R. orcid.org/0000-0002-0194-6653 and Hain, T. orcid.org/0000-0003-0939-3464 (2024) MUST: A MUltilingual Student-Teacher learning approach for low-resource speech recognition. In: Proceedings of 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 16-20 Dec 2023, Taipei, Taiwan. Institute of Electrical and Electronics Engineers (IEEE) , pp. 1-6. ISBN: 9798350306903 ISSN: 2997-6928 EISSN: 2997-6995

Abstract

Metadata

Item Type: Proceedings Paper
Authors/Creators:
Copyright, Publisher and Additional Information:

© 2023 The Authors. Except as otherwise noted, this author-accepted version of a paper published in Proceedings of 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/

Keywords: multilingual; knowledge distillation; automatic speech recognition; low-resource languages
Dates:
  • Published (online): 19 January 2024
  • Published: 19 January 2024
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 08 Aug 2025 10:19
Last Modified: 08 Aug 2025 10:21
Status: Published
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Identification Number: 10.1109/asru57964.2023.10389636
Open Archives Initiative ID (OAI ID):

Available Versions of this Item

Download

Export

Statistics