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|Title: ||Identification of vocal and voice disorders|
|Authors: ||Murugesa Pandiyan, Paulraj, Prof. Madya Dr.|
Sazali, Yaacob, Prof. Dr.
Mohd Rizon, Mohammed Juhari, Prof. Dr.
Sivanandam, S. N.
Muthusamy, Hariharan, Dr.
|Keywords: ||Acoustic analysis;Acoustic features;Neural network;Slope parameter;Gaussian activation function|
|Issue Date: ||25-Oct-2007|
|Publisher: ||Universiti Malaysia Perlis (UniMAP)|
|Citation: ||p. 779-784|
|Series/Report no.: ||Proceedings of the Conference on Applications and Design in Mechanical Engineering (CADME07)|
|Abstract: ||The discrimination of normal and pathological voices using noninvasive acoustic analysis helps to perform accurate identification of voice disorders and diagnoses of vocal and voice disease. Acoustic analysis is a non- invasive technique based on digital processing of the speech signal. In the recent years, acoustic analysis of normal and pathological voices have become increasingly interesting to researchers in laryngology and speech pathologies. This paper presents classification of pathological voices using neural network trained by Back propagation algorithm with slope parameter and BP with binary sigmoidal and Gaussian activation function. Simulation results indicate that the proposed algorithm provide better classification rate than conventional back propagation algorithm.|
|Description: ||Organized by Universiti Malaysia Perlis (UniMAP), 25th - 26th October 2007 at Putra Brasmana Hotel, Kuala Perlis, Perlis, Malaysia.|
|Appears in Collections:||Conference Papers|
Mohd. Rizon Mohamed Juhari, Prof. Ir. Dr.
Sazali Yaacob, Prof. Dr.
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