@article{,
author={Kocsor, Andr{\'a}s and T{\'o}th, L{\'a}szl{\'o}},
title={Application of Kernel-Based Feature Space Transformations and Learning Methods
to Phoneme Classification},
abstract={This paper examines the applicability of some learning techniques to the
classification of phonemes. The methods tested were artificial neural nets
(ANN), support vector machines (SVM) and Gaussian mixture modeling (GMM).
We compare these methods with a traditional hidden Markov phoneme model
(HMM), working with the linear prediction-based cepstral coefficient features
(LPCC). We also tried to combine the learners with linear/nonlinear and
unsupervised/supervised feature space transformation methods such as principal
component analysis (PCA), independent component analysis (ICA), linear
discriminant analysis (LDA), springy discriminant analysis (SDA) and their
nonlinear kernel-based counterparts. We found that the discriminative learners
can attain the efficiency of HMM, and that after the transformations they
can retain the same performance in spite of the severe dimension reduction.
The kernel-based transformations brought only marginal improvements compared
to their linear counterparts.},
journal={Applied Intelligence},
volume={21},
year={2004},
pages={129-142},
month={September},
number={2}
}