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This paper is focused on the optimization of features derived to characterize the acoustic perturbations encountered in a group of neurological disorders known as Dysarthria. The work derives a set of orthogonal features that enable acoustic analyses of dysarthric speech from eight different Dysarthria types. The feature set is composed by combinations of objective measurements obtained with digital signal processing algorithms and perceptual judgments of the most reliably perceived acoustic perturbations. The effectiveness of the features to provide relevant information of the disorders is evaluated with different classifiers enabling a classification rate up to 93.7%.