Paper Title
Computer Aided Diagnosis Of Parkinson's Disease Using Speech Signal

Parkinson’s disease is a progressive disease of the nervous system caused due to the dysfunction and break down of nerve cells in the brain called neurons. Various studies revealed that, about 90 percent of the people with Parkinson’s disease experienced changes in their voice or ability to make speech sounds. Main goal of this paper is to automatically detect whether the person is affected by Parkinson’s disease using voice. The classifier used here is support vector machine with linear kernel. Accuracy of the classification depends on the voice data samples, voice features and their number. Six voice features are used and 99.6% accuracy is achieved. Less the number of voice features more accurate will be the system. Database consisting of 40 healthy and 40 Parkinson’s voice samples are used. The experimental results reveal that the combination of feature extraction and support vector machine with linear kernel give promising results for the diagnosis of Parkinson’s disease. Keywords- Parkinson’s Disease; Support Vector Machine; Feature Extraction; Classification