Insights of neural representations in multi-banded and multi-channel convolutional transformers for end-to-end ASR

Ollerenshaw, A., Jalal, M.A. and Hain, T. orcid.org/0000-0003-0939-3464 (2022) Insights of neural representations in multi-banded and multi-channel convolutional transformers for end-to-end ASR. In: Proceedings of 2022 30th European Signal Processing Conference (EUSIPCO). 2022 30th European Signal Processing Conference (EUSIPCO), 29 Aug - 02 Sep 2022, Belgrade, Serbia. Institute of Electrical and Electronics Engineers (IEEE) , pp. 434-438. ISBN 9781665467995

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Copyright, Publisher and Additional Information: © 2022 by European Association for Signal Processing (EURASIP). Published by IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Keywords: end-to-end; automatic speech recognition; transformer; interpretability; convolutional neural networks
Dates:
  • Accepted: 12 July 2022
  • Published (online): 18 October 2022
  • Published: 18 October 2022
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: 28 Jul 2022 12:58
Last Modified: 15 Nov 2022 12:26
Published Version: https://ieeexplore.ieee.org/document/9909875
Status: Published
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
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