ML-based Smart Voice Analysis for Healthy and Pathological Voice Detection

Voice pathology is increasing dramatically, especially due to unhealthy social habits, being too much talkative, age factor or some kind of pathology in throat. Normally the people who speaks a lot for example, teachers or announcers etc. suffer from voice pathology in their elderly age. A research-oriented simulation project is developed, which will help a general physician to identify this voice pathology. It is work of specialist ENT doctor. This will help and assist general physician to identify pathology and to refer the patient to the specialist doctor. For implementing the work, a hybrid system is developed which is based on two models. For assisting general physician, the model first detects the pathology i.e. normal voice or pathological voice. In the second model the system will classify the type of disease present in the patient. The main objective of this research work is to investigate commonly used features available for feature extraction of voice pathology classification and detection. Then train the system using various machine learning techniques that can provide us with better pathology detection and classification rate. This paper focusses on developing an accurate Mel frequency Cepstral Coefficient feature extraction technique for classifying and detecting voice pathologies using a German Saarbrucken voice database. The system is trained by using four different kinds of machine learning algorithms which are Naïve Bayes, Random Forest, Support Vector Machine, K nearest neighbor and then finding out which algorithm perform well in both of these models. In this research validating the results and comparing it with previous research work is analyzed. Analysis of several different diseases and techniques are also provided in these studies