Robust Signal Subspace Speech Classifier

• Published in: IEEE signal processing letters Vol. 14; no. 11; pp. 844 - 847
• Main Authors: Tan, A.W.C., Rao, M.V.C., Daya Sagar, B.S.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-11-2007; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Additive white noise; Assembly; Classification; Classifiers; Distortion; Distortion measurement; Mathematical analysis; Matrices; Matrix methods; Noise measurement; Noise robustness; Prototypes; Robust distortion measures; Signal processing; Speech; Speech enhancement; speech modeling; Speech processing; Speech recognition; subspace methods; Subspaces
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2007.900036

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.