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.
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© 2001 Springer-Verlag Berlin Heidelberg
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Kocsor, A., Tóth, L., Paczolay, D. (2001). A Nonlinearized Discriminant Analysis and Its Application to Speech Impediment Therapy. In: Matoušek, V., Mautner, P., Mouček, R., Taušer, K. (eds) Text, Speech and Dialogue. TSD 2001. Lecture Notes in Computer Science(), vol 2166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44805-5_33
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DOI: https://doi.org/10.1007/3-540-44805-5_33
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