  Parkinson's disease is a neurodegenerative disorder characterized by the
presence of different motor impairments. Information from speech, handwriting,
and gait signals have been considered to evaluate the neurological state of the
patients. On the other hand, user models based on Gaussian mixture models -
universal background models (GMM-UBM) and i-vectors are considered the
state-of-the-art in biometric applications like speaker verification because
they are able to model specific speaker traits. This study introduces the use
of GMM-UBM and i-vectors to evaluate the neurological state of Parkinson's
patients using information from speech, handwriting, and gait. The results show
the importance of different feature sets from each type of signal in the
assessment of the neurological state of the patients.
