Recognition of pathological voices by Human Factor Cepstral Coefficients (HFCC)

Abstract

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 work, 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.