Application of Kernel-Based Feature Space Transformations and Learning Methods to Phoneme Classification

Application of Feature Transformation and Learning Methods in Phoneme Classification

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. 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...

Input space versus feature space in kernel-based methods

This paper collects some ideas targeted at advancing our understanding of the feature spaces associated with support vector (SV) kernel functions. We first discuss the geometry of feature space. In particular, we review what is known about the shape of the image of input space under the feature space map, and how this influences the capacity of SV methods. Following this, we describe how the me...

An application of minimum classification error to feature space transformations for speech recognition

The use of signal transformations is a necessary step for feature extraction in pattern recognition systems. These transformations should take into account the main goal of pattern recognition: the error-rate minimization. In this paper we propose a new method to obtain feature space transformations based on the Minimum Classification Error criterion. The goal of these transformations is to obt...

A Comparative Study of Several Feature Transformation and Learning Methods for Phoneme Classification

Abstract. 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, as a reference, an HMM recog...

Linear Feature Transformations in Slovak Phoneme-Based Continuous Speech Recognition