Evaluation of the effectiveness and efficiency of state-of-the-art features and models for automatic speech recognition error detection

El Hannani, A., Errattahi, R., Salmam, F.Z. et al. (2 more authors) (2021) Evaluation of the effectiveness and efficiency of state-of-the-art features and models for automatic speech recognition error detection. Journal of Big Data, 8. 5. ISSN 2196-1115

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Copyright, Publisher and Additional Information: © The Author(s) 2021. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Keywords: Automatic Speech Recognition; Confidence estimation; ASR error detection; ASR error type classification; Recurrent Neural Network; Multi-Genre Broadcast
Dates:
  • Accepted: 4 December 2020
  • Published (online): 6 January 2021
  • Published: 6 January 2021
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: 09 Feb 2021 16:34
Last Modified: 09 Feb 2021 16:34
Status: Published
Publisher: Springer Science and Business Media LLC
Refereed: Yes
Identification Number: https://doi.org/10.1186/s40537-020-00391-w

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