2001 | OriginalPaper | Buchkapitel
A Nonlinearized Discriminant Analysis and Its Application to Speech Impediment Therapy
verfasst von : András Kocsor, László Tóth, Dénes Paczolay
Erschienen in: Text, Speech and Dialogue
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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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.