Glottal pathology discrimination using ANN and SVM

Use of modern technological advances in real-time biomedical analysis is very crucial. Current work focuses on glottal pathology discrimination based on non-invasive speech analysis techniques. Primary set back in developing such method is irregular performance depreciation of several state of the art acoustic features. To excuse such problems, we have used glottal to noise excitation ratio, which predicts the breathiness quotient of the speech signal and is supported by characteristic mean pitch value. To build a judicial model, we have used Artificial Neural Network (ANN) and Support Vector Machine (SVM). Categorization performance is compared using well known parameters like true positive rate, true negative rate and accuracy. Results of the analysis show slightly favored performance for SVM based decisive system.