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Speech is being considered a pervasive and costless means to detect and monitor neurodegenerative disease progression. Many different approaches have been reported to differentiate normative subject speech from neurodegenerative patient speech. Most of them are focussed on statistical pattern recognition approaches to improve detection results on a baseline, considering only patient speech and normative controls. The definition of a normative control is not well established in itself, usually being subjects free of any pathology aligned in the same age range as patients. But one question which is not taken into account is the effects of aging in healthy controls, as usually neurodegenerative diseases may include mostly patients affected by certain effects, as dysphonia or dysarthria, as a consequence of aging. The present research introduces a methodology based on information theory to compare the effects produced by aging dysarthria with those due to Parkinson’s Disease, using the statistical distribution of speech articulation kinematics as a marker. On the one hand, it may be concluded that articulation kinematics is substantially different for PD and HC with respect to normative subjects. On the other hand, this does not seem to be the case between PD and HC subjects, as these subsets may share some dysarthric features which may be contributed more by aging than by neuromotor degeneration. This differentiation problem needs to be evaluated as well in the case of phonation features, otherwise there will not be full guarantee in using phonation features to assess neuromotor degeneration. In this sense new methodologies have to be designed to distinguish neurodegenerative from aging speech granting better guarantees.

Comparing Parkinson's Disease Dysarthria and Aging Speech using Articulation Kinematics