Voice Pathology Recognition and Classification using Noise Related Features

Nowadays, the diseases of the voice increase because of bad social habits and the misuse of voice. These pathologies should be treated from the beginning. Indeed, it is no longer necessary that the diseases of the voice lead to affect the quality of the voice as heard by a listener. The most useful tool for diagnosing such diseases is the Acoustic analysis. We present in this work, new expression parameters in order to clarify the description of the vocal signal. These parameters help to classify the unhealthy voices. They describes essentially the fundamental frequency F0, the Harmonics-to-Noise report HNR, the report Noise to Harmonics Ratio NHR and Detrended Fluctuation Analysis (DFA). The classification is performed on two Saarbruecken Voice and MEEI pathological databases using HTK classifiers. We can classify them into two different type: the first classification is binary which is used for the normal and pathological voices, the second one is called a four-category classification used in spasmodic, polyp, nodule and normal female voices and male speakers. And we studied the effects of these new parameters when combined with the MFCC, Delta, Delta second and Energy coefficients.

@article{Rabeh2018,
  title     = {Voice Pathology Recognition and Classification using Noise Related Features},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {11},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {HAMDI Rabeh and HAJJI Salah and CHERIF Adnane},
  doi       = {10.14569/IJACSA.2018.091112},
  url       = {https://doi.org/10.14569/IJACSA.2018.091112}
}