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 (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.

Export metadata

Additional Services

Search Google Scholar
Metadaten
Author:J. C. Vasquez-Correa, Tobias BockletORCiD, J. R. Orozco-Arroyave, E. Nöth
DOI:https://doi.org/10.1109/icassp40776.2020.9054348
ISBN:978-1-5090-6631-5
Parent Title (English):ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publisher:IEEE
Document Type:conference proceeding (article)
Language:English
Release Date:2024/07/05
Tag:GMM-UBM; Parkinson’s disease; gait analysis; handwriting analysis; ivectors; speech analysis
Pagenumber:6544 - 6548
First Page:6544
Last Page:6548
institutes:Fakultät Informatik
Verstanden ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.