Novel VTEO Based Mel Cepstral Features for Classification of Normal and Pathological Voices

In this paper, novel Variable length Teager Energy Operator (VTEO) based Mel cepstral features, viz., VTMFCC are proposed for automatic classification of normal and pathological voices. Experiments have been carried out using this proposed feature set, MFCC and their score-level fusion. Classification was performed using a 2nd order polynomial classifier on a subset of the MEEI database. The equal error rate (EER) on fusion was 3.2% less than EER of MFCC alone which was used as the baseline. Effectiveness of the proposed feature-set was also investigated under degraded conditions using the NOISEX-92 database for babble and high frequency channel noise.