Citation

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

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Abstract

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.

Item Type:	Article
Subjects:	T Technology > T Technology (General) Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	26 Sep 2011 02:50
Last Modified:	18 Feb 2014 02:27
URII:	http://shdl.mmu.edu.my/id/eprint/2988

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