摘要：Human speech is a means of communication that is very important in our daily lives. It is characterized by its great ability to transmit our ideas, our emotions, our personality etc. So, any alteration of the voice can prevent the person from exercising his professional and daily life naturally. It is for these reasons that it is very necessary to implement systems for detecting and classifying vocal pathologies. These automatic systems can help clinicians customize and detect the existence of any vocal pathology. In this context, several tools have been introduced to achieve early detection of voice disorders. Among these tools are the Human Factor Cepstral Coefficients (HFCC) combined with prosodic parameters, the Noise-Harmonic Ratio (NHR), the Harmonic-Noise Ratio (HNR), analysis of trend Fluctuations (DFA) and Fundamental frequency (F0). These parameters are introduced and calculated in every frame. In this study, we used a variation of HFCC called Equivalent Rectangular Bandwidth (ERB) to study the effects of HFCC on the classification of pathological voices. Using the HTK classifiers, the classification is carried out on two pathological databases, Massachusetts Eye and Ear Infirmary (MEEI) and Saarbruecken Voice Database (SVD). To assess the performance of the system, we used sensitivity and specificity.
