Robust Signal Subspace Speech Classifier

A speech model inspired by the signal subspace approach was recently proposed as a speech classifier with modest results. The method entails, in general, the assemblage of a set of subspace trajectories that consist of the right singular vectors of measurement matrices of the signal under considerat...

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Main Authors: Tan, Alan W. C., Rao, M. V. C., Sagar, B. S. Daya
Format: Article
Language:English
Published: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC 2007
Subjects:
Online Access:http://shdl.mmu.edu.my/2988/
http://shdl.mmu.edu.my/2988/1/1018.pdf
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author Tan, Alan W. C.
Rao, M. V. C.
Sagar, B. S. Daya
author_facet Tan, Alan W. C.
Rao, M. V. C.
Sagar, B. S. Daya
author_sort Tan, Alan W. C.
building MMU Institutional Repository
collection Online Access
description A speech model inspired by the signal subspace approach was recently proposed as a speech classifier with modest results. The method entails, in general, the assemblage of a set of subspace trajectories that consist of the right singular vectors of measurement matrices of the signal under consideration. Given an unknown signal, a simple distortion measure then applies in the classification procedure to pick the best matched class prototype. This letter examines the issue of robustness in the subspace classification scheme. Borrowing an important result on noisy measurement matrices, this letter formally establishes the notion of robustness in subspace classification and proceeds to propose a class of robust distortion measures for signal subspace models. Simulation results of subspace classifiers implementing the new distortion measures in an isolated digit speech recognition problem reveal no degradation in recognition accuracy, even under low SNR conditions.
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spelling mmu-29882014-02-18T02:27:07Z http://shdl.mmu.edu.my/2988/ Robust Signal Subspace Speech Classifier Tan, Alan W. C. Rao, M. V. C. Sagar, B. S. Daya T Technology (General) QA75.5-76.95 Electronic computers. Computer science A speech model inspired by the signal subspace approach was recently proposed as a speech classifier with modest results. The method entails, in general, the assemblage of a set of subspace trajectories that consist of the right singular vectors of measurement matrices of the signal under consideration. Given an unknown signal, a simple distortion measure then applies in the classification procedure to pick the best matched class prototype. This letter examines the issue of robustness in the subspace classification scheme. Borrowing an important result on noisy measurement matrices, this letter formally establishes the notion of robustness in subspace classification and proceeds to propose a class of robust distortion measures for signal subspace models. Simulation results of subspace classifiers implementing the new distortion measures in an isolated digit speech recognition problem reveal no degradation in recognition accuracy, even under low SNR conditions. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC 2007-11 Article NonPeerReviewed text en http://shdl.mmu.edu.my/2988/1/1018.pdf Tan, Alan W. C. and Rao, M. V. C. and Sagar, B. S. Daya (2007) Robust Signal Subspace Speech Classifier. IEEE Signal Processing Letters, 14 (11). pp. 844-847. ISSN 1070-9908 http://dx.doi.org/10.1109/LSP.2007.900036 doi:10.1109/LSP.2007.900036 doi:10.1109/LSP.2007.900036
spellingShingle T Technology (General)
QA75.5-76.95 Electronic computers. Computer science
Tan, Alan W. C.
Rao, M. V. C.
Sagar, B. S. Daya
Robust Signal Subspace Speech Classifier
title Robust Signal Subspace Speech Classifier
title_full Robust Signal Subspace Speech Classifier
title_fullStr Robust Signal Subspace Speech Classifier
title_full_unstemmed Robust Signal Subspace Speech Classifier
title_short Robust Signal Subspace Speech Classifier
title_sort robust signal subspace speech classifier
topic T Technology (General)
QA75.5-76.95 Electronic computers. Computer science
url http://shdl.mmu.edu.my/2988/
http://shdl.mmu.edu.my/2988/
http://shdl.mmu.edu.my/2988/
http://shdl.mmu.edu.my/2988/1/1018.pdf