Dimensionality reduction methods applied to both magnitude and phase derived features

A number of previous studies have shown that speech sounds may have an intrinsic low dimensional structure. Such studies have focused on magnitude-based features ignoring phase information, as is the convention in many speech processing applications. In this paper dimensionality reduction methods are applied to MFCC and modified group delay function (MODGDF) features derived from the magnitude and phase spectrum, respectively. The low dimensional structure of these representations is examined and a method to combine these features is detailed. Results show that both magnitude and phase derived features have a low dimensional structure. MFCCs are found to offer higher accuracy than MODGDFs in phone classification tasks. Results indicate that combining MFCCs and MODGDFs gives improvements for phone classification. PCA is shown to be capable of efficiently combining MFCCs and MODGDFs for improved classification accuracy without large increases in feature dimensionality.