Temporal energy and correlation features from Nyquist filter bank for text-independent speaker identification

This paper demonstrates the use of two new methods of feature extraction called temporal energy of subband cepstral coefficient (TESBCC) and temporal correlation of subband cepstral coefficient (TCSBCC) for 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 these new feature extraction techniques comes from a new transformation which is based on the Nyquist filter bank. We have proposed this transformation from speaker identification perspective. TESBCC and TCSBCC have 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. TESBCC feature set has 7.88% higher average accuracy compared to the MFCC feature set. Similarly TCSBCC feature set has 5.23% higher average accuracy compared to the MFCC feature set.