Application of feedforward neural network for the classification of pathological voices
Sazali, Yaacob, Prof. Dr.
Murugesa Padiyan, Paulraj, Dr.
Mohd Rizon, Mohammed Juhari, Prof. Dr.
Muthusamy, Hariharan, Dr.
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This paper present the application of feed forward neural network for the classification of pathological voices based on the on the acoustic analysis and EGG features. Acoustic analysis is a non-invasive technique based on digital processing of the speech signal. Electroglottography is a method of obtaining vibration signal related to the laryngeal phonatory function. The Electroglottograph (EGG) is an instrument that register the contact between the vocal folds as a time-varying signal. The time domain voice parameters are computed from the extracted pitch data. In this paper, a Feedback Neural Network is employed for the classification of pathological voices. The Acoustic parameters extracted from the speech signal and the features from the Electroglottography from the input to the neural network distinguish the voice as pathological or a non-pathological voice.