Automatic assessment of speech impairment is a cutting edge topic in Parkinson's disease (PD) detection. Patients usually face loss of prosody, volume and clarity, which results in a dysfunction of the different levels involved in speech production. This condition is clinically referred as dysarthria and is characterized by alterations in speed, volume, tone, range or precision of movements necessary for voice control. Despite a corroborated methodology is currently employed by the clinician to asses the presence and the level of Parkinson's disease, the development of an automatic tool able to reduce the operator-dependency is still an hot research topic. Furthermore, as far as we know, automatic methodologies for PD detection make mainly use of vocalization exercises, and the set of features to be extracted is demonstrated being task-dependent.