Detection of Voice Disorder Using Machine Learning

Voice disorder is a health issue that is frequently encountered, however, many patients either cannot afford to visit a professional doctor or neglect to take good care of their voice . In order to give a patient a preliminary diagnosis without using professional medical devices, previous research has shown that the detection of voice disorders can be carried out by utilizing machine learning and acoustic features extracted from voice recordings. Considering the increasing popularity of machine learning and feature learning, this study explores the possibilities of using these methods to assign voice recordings into one of the two classes Normal and Pathological . While the results show the general viability of machine learning and for the automatic recognition of voice disorder, they also demonstrate the shortcomings of the existing datasets for this task such as insufficient dataset size and lack of generality. The best accuracy in voice diseases detection is achieved by the(CNN). The proposed work uses spectrogram of voice recordings from a voice database as the input to a Convolutional Neural Network (CNN) for automatic feature extraction and classification of disordered and normal voice.