Title :
Voice disorders classification using multilayer neural network
Author :
Salhi, Lotfi ; Mourad, Talbi ; Cherif, Adnéne
Author_Institution :
Signal Process. Lab., Sci. Fac. of Tunis, Tunis
Abstract :
In this paper we present a new method for voice disorders classification based on multilayer neural network (MNN). The processing algorithm is based on a hybrid technique which uses the wavelets energy coefficients as input of the MNN. The training step uses a speech database of several pathological and normal voices collected from the national hospital ldquoRabta - Tunisrdquo 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.
Keywords :
neural nets; vocoders; wavelet transforms; hybrid technique; multilayer neural network; speech database; voice disorders classification; wavelets energy coefficients; Cepstral analysis; Cepstrum; Multi-layer neural network; Neural networks; Parkinson´s disease; Pathology; Signal analysis; Signal processing; Speech analysis; Speech processing;
Conference_Titel :
Signals, Circuits and Systems, 2008. SCS 2008. 2nd International Conference on
Conference_Location :
Monastir
Print_ISBN :
978-1-4244-2627-0
Electronic_ISBN :
978-1-4244-2628-7
DOI :
10.1109/ICSCS.2008.4746953