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

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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 2nd 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 2nd order polynomial classification.