Publications:Fusing Various Audio Feature Sets for Detection of Parkinson's Disease from Sustained Voice and Speech Recordings


Do not edit this section

Keep all hand-made modifications below

Title Fusing Various Audio Feature Sets for Detection of Parkinson’s Disease from Sustained Voice and Speech Recordings
Author Evaldas Vaiciukynas and Antanas Verikas and Adas Gelzinis and Marija Bacauskiene and Kestutis Vaskevicius and Virgilijus Uloza and Evaldas Padervinskis and Jolita Ciceliene
Year 2016
PublicationType Journal Paper
Journal Lecture Notes in Computer Science
Conference 18th International Conference, SPECOM 2016, Budapest, Hungary, August 23-27, 2016
Diva url
Abstract The aim of this study is the analysis of voice and speech recordings for the task of Parkinson’s disease detection. Voice modality corresponds to sustained phonation /a/ and speech modality to a short sentence in Lithuanian language. Diverse information from recordings is extracted by 22 well-known audio feature sets. Random forest is used as a learner, both for individual feature sets and for decision-level fusion. Essentia descriptors were found as the best individual feature set, achieving equal error rate of 16.3 % for voice and 13.3 % for speech. Fusion of feature sets and modalities improved detection and achieved equal error rate of 10.8 %. Variable importance in fusion revealed speech modality as more important than voice. © Springer International Publishing Switzerland 2016