Parkinson disease is a neurodegenerative disorder characterized by a slow but progressive evolution. Even if it mainly involves the motor system, issues with the phonatory system have been noticed. The patient loses full control of the speech apparatus; are noticed uncontrolled repetitions, incorrect articulation of words and a weakening of the voice speech. In recent years, non-invasive techniques based on speech signal processing have been developed for the purpose of early diagnosis and to monitor the effects of pharmacological and neuro stimulation therapies. This thesis work can be considered a primary step of a study focused on improving the models under analysis by increasing the database of the monitored subjects and identifying repeatable patterns using a weighted classification algorithm based on the estimate error of the feature extraction algorithm.