Pitch period and amplitude perturbations are widely used parameters to discriminate normal and voice disorder speech. Instantaneous pitch period and amplitude of glottal vibrations directly from the speech waveform may not give an accurate estimation of jitter and shimmer. In this paper, the significance of epochs (glottal closure instants) and strength of excitation (SoE) derived from the zero-frequency filter (ZFF) are exploited to discriminate the voice disorder and normal speech. Pitch epoch derived from ZFF is used to compute the jitter, and SoE derived around each epoch is used compute the shimmer. The derived epoch-based features are analyzed on the some of the voice disorders like Parkinson's disease, vocal fold paralysis, cyst, and gastroesophageal reflux disease. The significance of proposed epoch-based features for discriminating normal and pathological voices is analyzed and compared with the state-of-the-art methods using a support vector machine classifier. The results show that epoch-based features performed significantly better than other methods both in clean and noisy conditions. Copyright © 2017 ISCA.