Manifold learning-based phoneme recognition

Recently manifold learning algorithms researches have been motivated by the idea of modelling high dimensional data using approximate low dimensional submanifold of the original space. In this paper, two manifold learning algorithms, locally linear embedding (LLE) and isometric feature mapping (Isomap), are proposed to apply to speech phoneme feature data extracted from TIMIT corpus in an effort to perform nonlinear dimensionality reduction for yielding low dimensional features capable of discriminating between phonemes. Compared with these manifold learning algorithms, the traditional principal component analysis (PCA) is also used to perform linear dimensionality reduction within speech phoneme feature data. The resulting features are evaluated in support vector machines (SVM)-based phoneme recognition experiments. Experiment results indicate that manifold learning algorithms are effective for identifying phoneme using low dimensional phoneme feature data obtained from the original high dimensional phoneme feature space.