Novel Variable length Teager Energy Based features for person recognition from their hum

Most of the state-of-the-art voice biometrics systems use the natural speech signal (either read speech or spontaneous or contextual speech) from the subjects. In this paper, an attempt is made to identify speakers from their hum. A new feature set, viz., Variable length Teager Energy Based Mel Frequency Cepstral Coefficients (VTMFCC) is proposed for this problem. Experiments have been carried out for person identification and verification task using Linear Prediction Cepstral Coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC) with polynomial classifier of 2^nd order approximation. It is shown that the speaker identification rate for proposed feature set outperforms LPCC by 13.6% and is competitive over baseline MFCC. For speaker verification, a reduction in equal error rate (EER) by 1.73% is achieved when a score-level fusion system is employed by combining evidence from MFCC and VTMFCC.