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Multiresolution features and polynomial kernel subspace approach for isolated word recognition

This paper describes polynomial kernel subspace approach to Isolated Word Recognition (IWR) systems. Auditory motivated wavelet packet transform is used to derive the desirable speech features. This approach represents speech features as the projection of the Wavelet Packet Parameters (WPP) mapped into a feature space via a non-linear mapping onto the principal components called Kernel Fisher Discriminant (KFD). The nonlinear mapping between the input space and the feature space is implicitly performed using the kernel-trick. This nonlinear mapping using KFD increases the discrimination ability of a pattern classifier. The use of Mel-scale based and Bark-scale based wavelet packet trees for feature extraction process adds human auditory perception behavior to enhance the classification performance. Experimental results show that the proposed kernel based techniques are computationally efficient and performs well with less training data.