Musical Feature Based Classification of Parkinson's Disease Using Dysphonic Speech

Speech and voice disorders are one of the most significant biomarkers in early diagnosis of Parkinson's disease (PD). The development of an objective, reliable and effective prediction model is crucial for the early detection of PD by experts. The aim of this study is to investigate the effectiveness of musical features of voice recordings on PD and healthy subject discrimination issue. Extracted number of 41 musical features from the voice recordings of 28 PD and 62 healthy controls are used in the context of music information retrieval. These features are employed in the classification models either as a single large set or partitioned into smaller feature groups. Leave-one-subject-out (LOSO), leave-one-out (LOO) and 10-fold cross validation schemes are used while training and testing in support vector machine (SVM) and k-nearest neighbors (k-NN) classifiers by providing statistical measures. The effect of low, normal and high tone voice recordings is also studied separately, and the results show that using low-tone voice recordings may not be useful for discrimination of dysphonic voice. Despite using least number of features of all related schemes which use raw voice recordings, our proposed musical features with LOSO cross validation technique perform better accuracy results than the existing studies.