Voice pathology detection with predictable component analysis and wavelet decomposition model

In this study, we present a first attempt to apply the predictable component analysis to voice signals. This method projects the signals in components that optimize the normalized error variance, a quantity related to the predictability of the signals. Using the proposed method along with a wavelet decomposition model, we obtain voice signals estimations. These voice signals are sustained vowel "a" from three groups of people: those with healthy larynxes and others with nodule or Reinke\´s edema on the vocal folds. Using the Shannon entropy of the estimation errors, we present evidences that it is possible to detect the pathological cases using this measure.