Dimensionality reduction-based phoneme recognition

Abstract

In this paper, linear and nonlinear dimensionality reduction algorithms are proposed to speech phoneme data from TIMIT corpus in an effort to perform dimensionality reduction for yielding low dimensional features capable of discriminating between phonemes. The linear dimensionality reduction method, including principal component analysis (PCA) and linear discriminant analysis (LDA), and the nonlinear dimensionality reduction method, including locally linear embedding (LLE) and isometric feature mapping (Isomap), are investigated. The resulting features by dimensionality reduction are evaluated in support vector machines (SVM)-based phoneme recognition experiments. Experiment results indicate that traditional linear LDA and PCA techniques for dimensionality reduction are capable of outperforming nonlinear LLE and Isomap techniques for phoneme recognition.