Kernel Springy Discriminant Analysis and Its Application to a Phonological Awareness Teaching System

Abstract. 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. 1 A Phonological Awareness Teaching System The most important clue to the process of learning to read is the ability to separate and identify consecutive sounds that make words and to associate these sounds with its corresponding written form. To learn to read in a fruitful way young learners must, of course, also be aware of the phonemes and be able to manipulate them. Many children with learning disabilities have problems in their ability to process phonologica