Correlation Features and a Linear Transform Specific Reproducing Kernel

In this paper we introduce three ideas for phoneme classification: First, we derive the necessary steps to integrate linear transforms into the computation of reproducing kernels. This concept is not restricted to phoneme classification and can be applied to a wider range of research subjects. Second, in the context of support vector machine (SVM) classification, correlation features based on MFCC-vectors are proposed as a substitute for the common first and second derivatives, and the theory of the first part is applied to the new features. Third, an SVM structure in the spirit of phoneme states is introduced. Relative classification improvements of 40.67% compared to stacked MFCC features of equal dimension encourage further research in this direction.