PRS Winter 2019/20 Juan Camilo Vásquez-Correa - Representation Learning for Pathological Speech
Channel: Andreas Maier
Date: 03/03/2020

Parkinson's disease is a neurodegenerative disorder characterized by the presence of different motor impairments. Information from speech, handwriting, and gait signals has 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 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.

Toggle slide and presenter view: https://www.video.uni-erlangen.de/clip/id/12835
ICASSP Paper: https://arxiv.org/abs/2002.05412
Interspeech Paper: https://www.isca-speech.org/archive/Interspeech_2019/abstracts/1405.html
Source Code: https://github.com/jcvasquezc/AEspeech