Ragni, A. orcid.org/0000-0003-0634-4456 and Gales, M.J.F. (2011) Structured discriminative models for noise robust continuous speech recognition. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 22-27 May 2011, Prague, Czech Republic. IEEE , pp. 4788-4791. ISBN 9781457705380
Abstract
Recently there has been interest in structured discriminative models for speech recognition. In these models sentence posteriors are directly modelled, given a set of features extracted from the observation sequence, and hypothesised word sequence. In previous work these discriminative models have been combined with features derived from generative models for noise-robust speech recognition for continuous digits. This paper extends this work to medium to large vocabulary tasks. The form of the score-space extracted using the generative models, and parameter tying of the discriminative model, are both discussed. Update formulae for both conditional maximum likelihood and minimum Bayes' risk training are described. Experimental results are presented on small and medium to large vocabulary noise-corrupted speech recognition tasks: AURORA 2 and 4.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2011 IEEE. |
Keywords: | Structured model; Noise robustness; Context modelling; Conditional Maximum Likelihood; Minimum Phone Error |
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 15:19 |
Last Modified: | 15 Nov 2019 15:19 |
Published Version: | https://ieeexplore.ieee.org/abstract/document/5947... |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ICASSP.2011.5947426 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:152854 |