Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Fundamentals of speech recognition
Fundamentals of speech recognition
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
The nature of statistical learning theory
The nature of statistical learning theory
Neural networks for pattern recognition
Neural networks for pattern recognition
Kernel principal component analysis
Advances in kernel methods
New approximations of differential entropy for independent component analysis and projection pursuit
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Shrinking the tube: a new support vector regression algorithm
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Fast Independent Component Analysis in Kernel Feature Spaces
SOFSEM '01 Proceedings of the 28th Conference on Current Trends in Theory and Practice of Informatics Piestany: Theory and Practice of Informatics
Kernel Springy Discriminant Analysis and Its Application to a Phonological Awareness Teaching System
TSD '02 Proceedings of the 5th International Conference on Text, Speech and Dialogue
Kernel partial least squares regression in reproducing kernel hilbert space
The Journal of Machine Learning Research
Classes of kernels for machine learning: a statistics perspective
The Journal of Machine Learning Research
Kernel independent component analysis
The Journal of Machine Learning Research
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Whitening-based feature space transformations in a speech impediment therapy system
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
Speaker normalization via springy discriminant analysis and pitch estimation
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
A multi-threshold segmentation approach based on Artificial Bee Colony optimization
Applied Intelligence
A comparison of nature inspired algorithms for multi-threshold image segmentation
Expert Systems with Applications: An International Journal
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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.