A real-time application to detect human voice disorders

This paper proposes an automatic method to differentiate healthy and pathological human voices in real-time to construct more accurate medical decisions and allow patients with disorders to seek early medical assistance. The main problem encountered in voice recognition is to identify the appropriate features and a classifier. To address a solution to this problem, this paper introduced a fast and accurate voice recognition method by approaching the MFCC audio processing features with three machine learning algorithms; Decision Tree (DT), Support Vector Machine (SVM), and Artificial Neural Network (ANN). As a result, ANN classifier performed the highest classification accuracy of 87.5%, whilst DT and SVM performed 62.5% and 50%, respectively.