Parkinson's Disease is a neurodegenerative disease affecting millions of people globally, most of whom present difficulties producing speech sounds. In this paper, we describe a system to identify the degree to which a person suffers from the disease. We use a number of automatic phone recognition-based features and we augment these with i-vector features and utterance-level acoustic aggregations. On the Interspeech 2015 ComParE challenge corpus, we find that these features allow for prediction well above the challenge baseline, particularly under cross-validation evaluation.
Cite as: An, G., Brizan, D.G., Ma, M., Morales, M., Syed, A.R., Rosenberg, A. (2015) Automatic recognition of unified Parkinson's disease rating from speech with acoustic, i-vector and phonotactic features. Proc. Interspeech 2015, 508-512, doi: 10.21437/Interspeech.2015-185
@inproceedings{an15_interspeech,
author={Guozhen An and David Guy Brizan and Min Ma and Michelle Morales and Ali Raza Syed and Andrew Rosenberg},
title={{Automatic recognition of unified Parkinson's disease rating from speech with acoustic, i-vector and phonotactic features}},
year=2015,
booktitle={Proc. Interspeech 2015},
pages={508--512},
doi={10.21437/Interspeech.2015-185}
}