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

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{{PublicationSetupTemplate|PID=955931
|Name=Vaiciukynas, Evaldas (Kaunas University of Technology, Kaunas, Lithuania);Verikas, Antanas [av] [0000-0003-2185-8973] (Högskolan i Halmstad [2804], Akademin för informationsteknologi [16904], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], CAISR Centrum för tillämpade intelligenta system (IS-lab) [13650]) (Kaunas University of Technology, Kaunas, Lithuania);Gelzinis, Adas (Kaunas University of Technology, Kaunas, Lithuania);Bacauskiene, Marija (Kaunas University of Technology, Kaunas, Lithuania);Vaskevicius, Kestutis (Kaunas University of Technology, Kaunas, Lithuania);Uloza, Virgilijus (Lithuanian University of Health Sciences, Kaunas, Lithuania);Padervinskis, Evaldas (Lithuanian University of Health Sciences, Kaunas, Lithuania);Ciceliene, Jolita (Lithuanian University of Health Sciences, Kaunas, Lithuania)
|Title=Fusing Various Audio Feature Sets for Detection of Parkinson’s Disease from Sustained Voice and Speech Recordings
|PublicationType=Journal Paper
|ContentType=Refereegranskat
|Language=eng
|Journal=Lecture Notes in Computer Science
|JournalISSN=0302-9743
|Status=published
|Volume=9811
|Issue=
|HostPublication=
|Conference=18th International Conference, SPECOM 2016, Budapest, Hungary, August 23-27, 2016
|StartPage=328
|EndPage=337
|Year=2016
|Edition=
|Pages=
|City=Heidelberg
|Publisher=Springer Berlin/Heidelberg
|Series=
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|DOI=http://dx.doi.org/10.1007/978-3-319-43958-7_39
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|NBN=urn:nbn:se:hh:diva-31872
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|Keywords=Parkinson’s disease;Audio signal processing;OpenSMILE;Essentia;MPEG-7;jAudio;YAAFE;Random forest;Information fusion
|Categories=Språkteknologi (språkvetenskaplig databehandling) (10208)
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|Notes=<p>Funding: Research Council of Lithuania (No. MIP-075/2015)</p>
|Abstract=<p>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</p>
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|CreatedDate=2016-08-27
|PublicationDate=2016-08-27
|LastUpdated=2016-08-29
|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:955931}}
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