Acoustic analysis of the speech signal enables non-intrusive, affordable, unbiased and fast assessment of voice pathologies. This assessment provides complimentary information to otolaryngologist for preliminary diagnosis of pathological larynx. Several voice impairment assessment systems focused on acoustic analysis have been introduced in recent years. Nevertheless, these systems are tested using only one or two datasets and are not independent of database and human bias. In this paper, a unified wavelet based framework is suggested for evaluating voice disorders, which is independent of database and human bias. Stationary wavelet transform (SWT) is used to decompose the speech signal, since it offers good time and frequency localization. Energy and statistical features are extracted from each sub-band after multilevel decomposition. Higher the decomposition level, higher is the order of feature …