This paper studies the application of automatic phoneme classification
to the computer-aided training of the speech and hearing handicapped.
In particular, we focus on how efficiently discriminant analysis
can reduce the number of features and increase classification
performance. A nonlinear counterpart of Linear Discriminant Analysis,
which is a general purpose class specific feature extractor, is
presented where the nonlinearization is carried out by employing
the so-called 'kernel-idea'. Then, we examine how this nonlinear
extraction technique affects the efficiency of learning algorithms
such as Artificial Neural Network and Support Vector Machines.
