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.
Bibliographic reference. An, Guozhen / Brizan, David Guy / Ma, Min / Morales, Michelle / Syed, Ali Raza / Rosenberg, Andrew (2015): "Automatic recognition of unified parkinson's disease rating from speech with acoustic, i-vector and phonotactic features", In INTERSPEECH-2015, 508-512.