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.