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This paper examines the applicability of some learning techniques for speech recognition, more precisely, for the classification of phonemes represented by a particular segment model. The methods compared were TiMBL (the IB1 algorithm), C4.5 (ID3 tree learning), OC1 (oblique tree learning), artificial neural nets (ANN), Gaussian mixture modeling (GMM) and,(More)
A substantial number of linear and nonlinear feature space transformation methods have been proposed in recent years. Using the so-called " kernel-idea " well-known linear techniques such as Principal Component Anal-ysis(PCA), Linear Discriminant Analysis(LDA) and Independent Component Analysis(ICA) can be non-linearized in a general way. The aim of this(More)
This paper presents an overview of the " AMOR " segment-based speech recognition system developed at the Research Group on Artificial Intelligence of the Hungarian Academy of Sciences. We present the preprocessing method, the features extracted from its output, and how segmentation of the input signal is done based on those features. We also describe the(More)
This paper presents an overview of the " OASIS " segment-based speech recognition system developed at the Research Group on Artificial Intelligence of the Hungarian Academy of Sciences. We present the preprocessing method, the features extracted from its output, and how segmentation of the input signal is done based on those features. We also describe the(More)
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