Exploiting Variable length Teager Energy Operator in melcepstral features for person recognition from humming

In this paper, we attempt voice biometrics problem using only humming signal rather than normal speech. This paper adapts a new feature extraction technique which exploits Variable length Teager Energy Operator (VTEO) onto subband filtered signal of Mel filterbank. This feature modifies structure of state-of-the-art feature set, viz., Mel Frequency Cepstral Coefficients (MFCC). In particular, a new energy measure, viz., VTEO is employed to compute subband energies of different time-domain subband signals. The features derived MFCCs to capture magnitude and phase spectrum information via VTEO are termed as MFCC-VTMP. Discriminatively-trained polynomial classifier of 2^nd order approximations is used as the basis for all experiments. MFCC-VTMP feature set is found to be better than MFCC for various evaluation factors such as order of polynomial classifier, dimension of feature vector, signal degradation conditions and class separability. % EER of MFCC and MFCC-VTMP are found to be 12.20% and 12.01%, respectively using 2^nd order polynomial classification.