Ragni, A. orcid.org/0000-0003-0634-4456 and Gales, M.J.F. (2012) Inference algorithms for generative score-spaces. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 25-30 Mar 2012, Kyoto, Japan. IEEE , pp. 4149-4152. ISBN 9781467300452
Abstract
Using generative models, for example hidden Markov models (HMM), to derive features for a discriminative classifier has a number of advantages including the ability to make the features robust to speaker and noise changes. An interesting attribute of the derived features is that they may not have the same conditional independence assumptions as the underlying generative models, which are typically first-order Markovian. For efficiency these features are derived given a particular segmentation. This paper describes a general algorithm for obtaining the optimal segmentation with combined generative and discriminative models. Previous results, where the features were constrained to have first-order Markovian dependencies, are extended to allow derivative features to be used which are non-Markovian in nature. As an example, inference with zero and first-order HMM score-spaces is considered. Experimental results are presented on a noise-corrupted continuous digit string recognition task: AURORA 2.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2012 IEEE. |
Keywords: | Structured discriminative model; generative score-space; inference |
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 12:49 |
Last Modified: | 15 Nov 2019 12:49 |
Published Version: | https://ieeexplore.ieee.org/abstract/document/6288... |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ICASSP.2012.6288832 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:152852 |