Gales, M.J.F., Knill, K.M. and Ragni, A. (2017) Low-resource speech recognition and keyword-spotting. In: Karpov, A., Potapova, R. and Mporas, I., (eds.) Speech and Computer : 19th International Conference, SPECOM 2017. 19th International Conference, SPECOM 2017, 12-16 Sep 2017, Hatfield, UK. Springer International Publishing , pp. 3-19. ISBN 9783319664286
Abstract
The IARPA Babel program ran from March 2012 to November 2016. The aim of the program was to develop agile and robust speech technology that can be rapidly applied to any human language in order to provide effective search capability on large quantities of real world data. This paper will describe some of the developments in speech recognition and keyword-spotting during the lifetime of the project. Two technical areas will be briefly discussed with a focus on techniques developed at Cambridge University: the application of deep learning for low-resource speech recognition; and efficient approaches for keyword spotting. Finally a brief analysis of the Babel speech language characteristics and language performance will be presented.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2017 Springer International Publishing. |
Keywords: | Prosody perception; Narrow versus broad focus; Japanese learners of English; L2 acquisition |
Dates: |
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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: | 15 Nov 2019 10:28 |
Last Modified: | 15 Nov 2019 10:28 |
Status: | Published |
Publisher: | Springer International Publishing |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-66429-3_1 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:152809 |