Multimodal I-vectors to Detect and Evaluate Parkinson's Disease

Nicanor Garcia, Juan Camilo Vásquez Correa, Juan Rafael Orozco-Arroyave, Elmar Nöth

Parkinson's Disease (PD) is a neurodegenerative disorder characterized by a variety of motor symptoms. PD patients show several motor deficits, including speech deficits, impaired handwriting and gait disturbances. In this work we propose a methodology to fuse i-vectors extracted from three different bio-signals: speech, handwriting and gait. These i-vectors are used to classify Parkinson's Disease patients and healthy controls and to evaluate the neurological state of the patients. Speech i-vectors are extracted from MFCCs, handwriting i-vectors are extracted from kinematic features and gait i-vectors are extracted from modified MFCCs computed from inertial sensor signals. Two fusion strategies are tested: concatenating the i-vectors of a subject to form a super-i-vector with information from the three bio-signals and score pooling. The proposed fusion methods leads to better classification results respect to the separate analysis with each bio-signal, reaching an accuracy of up to 85%.

 DOI: 10.21437/Interspeech.2018-2295

Cite as: Garcia, N., Vásquez Correa, J.C., Orozco-Arroyave, J.R., Nöth, E. (2018) Multimodal I-vectors to Detect and Evaluate Parkinson's Disease. Proc. Interspeech 2018, 2349-2353, DOI: 10.21437/Interspeech.2018-2295.

  author={Nicanor Garcia and Juan Camilo {Vásquez Correa} and Juan Rafael Orozco-Arroyave and Elmar Nöth},
  title={Multimodal I-vectors to Detect and Evaluate Parkinson's Disease},
  booktitle={Proc. Interspeech 2018},