Noise Robust Phonetic Classificationwith Linear Regularized Least Squares and Second-Order Features
Ryan M. Rifkin, Ken Schutte, Michelle Saad, Jake Bouvrie, Jim Glass · 2007
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.