Manifold Learning-Based Feature Transformation for Phone Classification

Abstract

This study aims to investigate approaches for low dimensional speech feature transformation using manifold learning. It has recently been shown that speech sounds may exist on a low dimensional manifold nonlinearly embedded in high dimensional space. A number of manifold learning techniques have been developed in recent years that attempt to discover this type of underlying geometric structure. The manifold learning techniques locally linear embedding and Isomap are considered in this study. The low dimensional representations produced by applying these techniques to MFCC feature vectors are evaluated in several phone classification tasks on the TIMIT corpus. Classification accuracy is analysed and compared to conventional MFCC features and those transformed with PCA, a linear dimensionality reduction method. It is shown that features resulting from manifold learning are capable of yielding higher classification accuracy than these baseline features. The best phone classification accuracy in general is demonstrated by feature transformation with Isomap.