No abstract

Parkinson's Disease (PD) is a neuro-degenerative disorder that affects primarily the motor system of the body. Besides other functions, the subject's speech also deteriorates during the disease, which allows for a non-invasive way of automatic screening. In this study, we represent the utterances of subjects having PD and those of healthy controls by means of the Fisher Vector approach. This technique is very common in the area of image recognition, where it provides a representation of the local image descriptors via frequency and high order statistics. In the present work, we used four frame-level feature sets as the input of the FV method, and applied (linear) Support Vector Machines (SVM) for classifying the speech of subjects. We found that our approach offers superior performance compared to classification based on the i-vector and cosine distance approach, and it also provides an efficient combination of machine learning models trained on different feature sets or on different speaker tasks.