Gammachirp Wavelet and Neural Network for Identification of Pathological Voices
In this study, we present a new method for voice disorders identification based on a gammachirp wavelet transform and Multilayer Neural Network (MNN). The processing algorithm is based on a hybrid technique which uses the gammachirp wavelets coefficients as input of the MNN. The training step uses a speech database of several pathological and normal voices collected from the national hospital Rabta-Tunis and was conducted in a supervised mode for discrimination of normal and pathology voices and in a second step identification between neural and vocal pathologies (Parkinson, Alzheimer, laryngeal, dyslexia…). Several simulation results will be presented in function of the disease and will be compared with the clinical diagnosis in order to have an objective evaluation of the developed tool.