Manifold Learning-Based Feature Transformation for Phone Classification

This paper investigates 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 techniques have been developed in recent years that attempt to discover the geometric structure of the underlying low dimensional manifold. The manifold learning techniques locally linear embedding and Isomap are considered in this study. The low dimensional feature representations produced by these techniques are applied to several phone classification tasks on the TIMIT corpus. Classification accuracy is analysed and compared to conventional MFCC features and PCA, a linear dimensionality reduction method, transformed features. 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.