Making use of the ubiquitous kernel notion, we present a new nonlinear
supervised feature extraction technique called Kernel Springy Discriminant
Analysis. We demonstrate that this method can efficiently reduce the number of features and increase classification performance.
The improvements obtained admittedly
arise from the nonlinear nature of the extraction technique developed
here. Since phonological awareness is a great importance in learning to read, a
computer-aided training system could be most beneficial in teaching young learners.
Naturally, our system employs an effective automatic phoneme recognizer
based on the proposed feature extraction technique.