Utilizing intelligent segmentation in isolated word recognition using a hybrid HTD-HMM
CISST '11 Proceedings of the 5th WSEAS international conference on Circuits, systems, signal and telecommunications
Translation Invariance in the Polynomial Kernel Space and Its Applications in kNN Classification
Neural Processing Letters
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This paper describes polynomial kernel subspace approach to Isolated Word Recognition (IWR) systems. Linear Predictive Coding (LPC) coefficients derived from wavelet sub-bands of speech frame were used as features. This approach represents mapping of speech features (input space) into a feature space via a non-linear mapping onto the principal components called Kernel Linear Discriminant Analysis (KLDA). The non-linear mapping between the input space and the feature space is implicitly performed using the kernel-trick. This nonlinear mapping using KLDA increases the discrimination ability of a pattern classifier. The use of Wavelet sub-band based LPC features (WLPC) provide low dimensional features which reduce the memory requirement and KLDA provides the fast classification and recognition. Experimental results obtained on isolated word database show that the proposed technique is computationally efficient and performs well with less training data.