A Nonlinearized Discriminant Analysis and its Application to Speech Impediment Therapy

Abstract. 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. 1 Speech Impediment Therapy and Real-Time Phoneme Classification This paper deals with the application of speech recognition to the computer-aided training of the speech and hearing handicapped. The program we present was designed to help in the speech training of the hearing impaired, where the goal is to support or replace their diminished auditory feedback with a visual one. But the program could als