Isolated Word Recognition Using Low Dimensional Features and Kernel Based Classification

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.