Discrimination of pathological voices using systole activated neural network
Murugesa Pandiyan, Paulraj, Prof. Madya Dr,
Sazali, Yaacob, Prof. Dr.
Hariharan, Muthusamy, Dr.
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The discrimination of normal and pathological voices using noninvasive acoustical analysis features helps speech specialits to perform accurate diagnoses of vocal and voices disease. Acoustic analysis is a non-invasive technique based on digital processing of the speech, acoustic analyses of normal and pathological voices have become increasingly interesting to researchers in ENT and speech pathologies. This paper presents discrimination of pathological voices using Artificial Neural Network for the accurate diagnosis of vocal and voices disease. A Neural network is trained using Back propagation algorithm with bipolar activation function and systole activation function. The neural network trained by using back propagation algorithm with systole activation function provides very promising classification accuracy of 99% to discriminate the voices as pathological or a non-pathological or a non-pathological voice accurately.