View source for Publications:Parkinson's Disease Detection from Speech Using Convolutional Neural Networks

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{{PublicationSetupTemplate|Author=Evaldas Vaiciukynas, Adas Gelzinis, Antanas Verikas, Marija Bacauskiene
|PID=1198161
|Name=Vaiciukynas, Evaldas (0000-0002-4769-4527) (Kaunas University of Technology, Kaunas, Lithuania);Gelzinis, Adas (0000-0002-0415-1434) (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);Bacauskiene, Marija (Kaunas University of Technology, Kaunas, Lithuania)
|Title=Parkinson’s Disease Detection from Speech Using Convolutional Neural Networks
|PublicationType=Conference Paper
|ContentType=Refereegranskat
|Language=eng
|Journal=
|JournalISSN=
|Status=
|Volume=233
|Issue=
|HostPublication=Smart objects and technologies for social good : Third International Conference, GOODTECHS 2017, Pisa, Italy, November 29-30, 2017, Proceedings
|Conference=Third EAI International Conference on Smart Objects and Technologies for Social Good, GOODTECHS 2017, Pisa, Italy, November 29-30, 2017
|StartPage=206
|EndPage=215
|Year=2018
|Edition=
|Pages=
|City=Cham
|Publisher=Springer
|Series=Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ; 233
|SeriesISSN=
|ISBN=978-3-319-76111-4
|Urls=
|ISRN=
|DOI=http://dx.doi.org/10.1007/978-3-319-76111-4_21
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|NBN=urn:nbn:se:hh:diva-36617
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|Keywords=Parkinson’s disease;Audio signal processing;Convolutional neural network;Information fusion
|Categories=Signalbehandling (20205)
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|Notes=<p>Funding: Research Council of Lithuania (No. MIP-075/2015)</p>
|Abstract=<p>Application of deep learning tends to outperform hand-crafted features in many domains. This study uses convolutional neural networks to explore effectiveness of various segments of a speech signal,? – text-dependent pronunciation of a short sentence, – in Parkinson’s disease detection task. Besides the common Mel-frequency spectrogram and its first and second derivatives, inclusion of various other input feature maps is also considered. Image interpolation is investigated as a solution to obtain a spectrogram of fixed length. The equal error rate (EER) for sentence segments varied from 20.3% to 29.5%. Fusion of decisions from sentence segments achieved EER of 14.1%, whereas the best result when using the full sentence exhibited EER of 16.8%. Therefore, splitting speech into segments could be recommended for Parkinson’s disease detection. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018.</p>
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|CreatedDate=2018-04-16
|PublicationDate=2018-04-16
|LastUpdated=2018-04-17
|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1198161}}
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