Noise Robust Phonetic Classificationwith Linear Regularized Least Squares and Second-Order Features

We perform phonetic classification with an architecture whose elements are binary classifiers trained via linear regularized least squares (RLS). RLS is a simple yet powerful regularization algorithm with the desirable property that a good value of the regularization parameter can be found efficient...

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Bibliographic Details
Published in2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07 Vol. 4; pp. IV-881 - IV-884
Main Authors Rifkin, R., Schutte, K., Saad, M., Bouvrie, J., Glass, J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2007
Subjects
Acoustic noise
Additive noise
Artificial Intelligence
Automatic speech recognition
Least squares methods
Noise robustness
Probability distribution
Resonance light scattering
Speech analysis
Speech recognition
Support vector machines
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Summary:We perform phonetic classification with an architecture whose elements are binary classifiers trained via linear regularized least squares (RLS). RLS is a simple yet powerful regularization algorithm with the desirable property that a good value of the regularization parameter can be found efficiently by minimizing leave-one-out error on the training set. Our system achieves state-of-the-art single classifier performance on the TIMIT phonetic classification task, (slightly) beating other recent systems. We also show that in the presence of additive noise, our model is much more robust than a well-trained Gaussian mixture model.
ISBN:9781424407279
1424407273
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2007.367211