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As acoustic signal generated from vocal folds is directly affected by vocal tract pathologies, it can be an effective tool for diagnosis purpose. In this work, we present an efficient method for voice pathology detection based on speech signal processing and machine learning techniques. In the proposed method, we used MFCC to represent the signal features, and we chose to combine GMM and SVMclassifiers to benefit from their generative and discriminative natures respectively. That is to exploit the similarity function of the RBF kernel to separate the GMM models representing normal and pathological voices. To further improve the separation, we used modified versions of the well known Kullback-leibler and Bhattacharyya distances. The modified distances, unlike the classical ones, do satisfy all metric axioms. As a result, we obtained an improvement of 2 % and 4 % in terms of sensitivity compared to using the classical Kullback-leibler and Bhattacharyya distances respectively. The Receiver Operating Curve (ROC) does illustrate the efficiency of the proposed method.

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