Fundamentals of speech recognition
Fundamentals of speech recognition
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A Discriminative Segmental Speech Model and Its Application to Hungarian Number Recognition
TDS '00 Proceedings of the Third International Workshop on Text, Speech and Dialogue
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
On kernel discriminant analyses applied to phoneme classification
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
| Hi-index | 0.00 |
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 howthis nonlinear extraction technique affects the efficiency of learning algorithms such as Artificial Neural Network and Support Vector Machines.