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

Comparison of user models based on GMM-UBM and i-vectors for speech, handwriting, and gait assessment of Parkinson's disease patients