Importance of Using Log Function to Reduce the Correlation between Features in a Multidimensional Feature Space for Text-Independent Speaker Identification

This paper demonstrates the relation between flatness index of the eigen values of the covariance matrix of the feature vectors and the correlation between the features in a multidimensional feature space. The constant distance loci of Mahalanobis metric has been used to interpret the relation. The intuitive interpretation of the flatness index and correlation has been given using 2D synthetic data. The usefulness of log function to reduce the correlation between features has been shown using synthetic data and real feature vectors extracted from speech data for text-independent speaker identification using MFCC, LFCC and IMFCC feature sets.