On the Use of Kernel PCA for Feature Extraction in Speech Recognition

This paper describes an approachfor feature extraction in speech recognition systems using kernel principal componentanalysis (KPCA). This approachconsists in representing speech features as the projection of the extracted speech features mapped into a feature space via a nonlinear mapping onto the principal components. The nonlinear mapping is implicitly performed using the kerneltrick, which is an useful way of not mapping the input space into a featurespace explicitly,makingthis mapping computationally feasible. Better results were obtained by using this approach when compared to the standard technique.