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

  • 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.

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Author:Juan Vasquez, Tobias Bocklet, JuanRafael Orozco, Elmar Nöth
DOI:https://doi.org/10.48550/arXiv.2002.05412
ArXiv Id:http://arxiv.org/abs/arXiv:2002.05412
Document Type:conference proceeding (article)
Language:English
Date of first Publication:2020/02/13
Reviewed:Begutachtet/Reviewed
Release Date:2024/07/03
Tag:Parkinson’s disease, GMM-UBM, i- vectors, gait analysis, handwriting analysis, speech analysis
Pagenumber:5
First Page:6544
Last Page:6548
institutes:Fakultät Informatik
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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