Acoustic analysis of speech signal enables automatic detection and classification of voice disorders along with its severity. This automatic assessment provides help to the clinician in initial diagnosis of pathological larynx in non-intrusive way. Voice pathologies damage the vocal cords and consequently alter the dynamics (fluctuation speed) of vocal cords. In this article, we have estimated glottal volume velocity waveform (GVVW) from the speech pressure waveforms of healthy and pathological subjects using quasi closed phase (QCP) glottal inverse filtering algorithm to capture altered dynamics of vocal cords. Closed-phase methods revealed notable stability in diverse voice qualities and sub-glottal pressures. The GVVW is the source of significant acoustical clues rooted in speech. The estimated GVVW is then parameterized by various time based, frequency based and Liljencrants–Fant (LF) model based glottal descriptors. Glottal descriptor’s vectors have been passed on to stochastic gradient descent (SGD) classifier for voice disorder evaluation. The normal pitch utterance of sustained vowel /a/ quarried from German, English, Arabic and Spanish voice databases is used. Information gain (IG) feature scoring technique is employed to select optimal descriptors and to rank them. Several intra and cross-database experiments were performed to explore the usefulness of glottal descriptors for voice disorder detection, severity detection and classification. Student’s t-tests were performed to validate the obtained results.