Voice Disorders Identification Using Multilayer Neural Network

Lotfi Salhi, Talbi Mourad, and Adnene Cherif
 Signal Processing Laboratory Sciences Faculty of Tunis, University Tunis ElManar, Tunisia

Abstract: In this paper we present a new method for voice disorders classification based on multilayer neural network. The processing algorithm is based on a hybrid technique which uses the wavelets energy coefficients as input of the multilayer neural network. 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 classification 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.

Received August 27, 2008; accepted December 28, 2008