A Nonlinearized Discriminant Analysis and its Application to Speech Impediment Therapy

TitleA Nonlinearized Discriminant Analysis and its Application to Speech Impediment Therapy
Publication TypeConference Paper
Year of Publication2001
AuthorsKocsor A, Tóth L, Paczolay D
Editor
Conference NameText, Speech and Dialogue : 4th International Conference, TSD 2001, LNAI vol. 2166
Pagination249-257
Date PublishedSeptember
PublisherSpringer-Verlag GmbH
Place PublishedZelezna Ruda, Czech Republic
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.