Comparison of User Models Based on GMM-UBM and I-Vectors for Speech, Handwriting, and Gait Assessment of Parkinson’s Disease Patients

Vasquez Correa J, Bocklet T, Orozco Arroyave JR, Nöth E (2020)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2020

Publisher: IEEE

Event location: Barcelona ES

ISBN: 9781509066315

DOI: 10.1109/ICASSP40776.2020.9054348

Abstract

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 (GMMUBM) 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.

Authors with CRIS profile

Juan Vasquez Correa Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) Juan Rafael Orozco Arroyave Professur für Informatik (Mustererkennung) Elmar Nöth Professur für Informatik (Mustererkennung)

Involved external institutions

Technische Hochschule Nürnberg "Georg Simon Ohm" DE Germany (DE)

How to cite

APA:

Vasquez Correa, J., Bocklet, T., Orozco Arroyave, J.R., & Nöth, E. (2020). Comparison of User Models Based on GMM-UBM and I-Vectors for Speech, Handwriting, and Gait Assessment of Parkinson’s Disease Patients. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Barcelona, ES: IEEE.

MLA:

Vasquez Correa, Juan, et al. "Comparison of User Models Based on GMM-UBM and I-Vectors for Speech, Handwriting, and Gait Assessment of Parkinson’s Disease Patients." Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona IEEE, 2020.

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