In biomedical acoustics, distortion in voice signals, commonly pre- sent during acquisition and transmission, adversely affects acoustic features extracted from pathological voice. Information on the type of distortion can help in compensating for its effects. This paper proposes a new approach to detecting four major types of commonly encountered distortion in remote analysis of pathological voice, na- mely background noise, reverberation, clipping and coding. In this approach, by applying factor analysis to Gaussian mixture model mean supervectors, distortions in variable-duration recordings are modeled by fixed-length, low-dimensional channel vectors. Then, linear discriminant analysis (LDA) is used to remove the remaining nuisance effects in the channel vectors. Finally, two different clas- sifiers, namely support vector machines and probabilistic LDA clas- sify the different types of distortion. Experimental results obtained using Parkinson’s voices, as an example of pathological voice, show 11.4% relative improvement in performance over systems which di- rectly use acoustic features for distortion classification.