Parkinson's disease is an incurable neurodegenerative disease to the present clinical knowledge. It is diagnosed mostly by exclusion tests. Numerous studies have confirmed that speech can be promising to suspect the presence of the disease. On the other hand, just a few researches discuss the appropriate length of the speech sample or the contribution of parts of the full-length recordings in the classification. Hence, we partitioned each original recording into four shorter samples. We trained linear and radial basis function (rbf) kernel Support Vector Machine (SVM) models separately for original recordings, each partitioned group and all partitioned samples together. We found no significant difference between the results of the rbf kernel models. However, we obtained significantly better results with a portion of the entire speech using linear kernel models. In conclusion, even a shorter piece of a longer speech may be adequate for classification
