Significant improvement in the closed set text-independent speaker identification using features extracted from Nyquist filter bank

This paper introduces the use of 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 which is based on the Nyquist filter bank. We have proposed this transform from speaker identification perspective. This new feature extraction technique has been compared with Mel-frequency cepstral coefficient (MFCC) feature both theoretically and practically. Experimental evaluation was conducted on POLYCOST database with 130 speakers using Gaussian mixture speaker model. On clean speech the proposed feature set has 11.5% higher average accuracy compared to the MFCC feature set. For noisy speech also the proposed feature set performs significantly better than the MFCC feature set.