A New Transform for Robust Text-Independent Speaker Identification

This paper proposes a new method of feature extraction for robust text-independent speaker identification. The focus of this work is on applications which yield higher identification accuracy without increasing the computational effort. The impetus for this new feature extraction technique comes from a new transformation. We have proposed this transform from speaker identification perspective. A complete experimental evaluation was conducted on a database of 61 speakers with Gaussian mixture speaker model. This new feature extraction technique has been compared with mel-frequency cepstral coefficient (MFCC) feature. Evaluation results show, that the new feature provides better identification accuracy than the MFCC feature. The discrimination capability of the feature sets have been evaluated statistically, using F-ratio and J-measure. Experimental results show that the new feature set is much more discriminative than the MFCC feature set.