Convolutional Neural Networks and a Transfer Learning Strategy to Classify Parkinson’s Disease from Speech in Three Different Languages

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00335428" target="_blank" >RIV/68407700:21230/19:00335428 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-030-33904-3_66" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-33904-3_66</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-33904-3_66" target="_blank" >10.1007/978-3-030-33904-3_66</a>

Result language
angličtina
Original language name
Convolutional Neural Networks and a Transfer Learning Strategy to Classify Parkinson’s Disease from Speech in Three Different Languages
Original language description
Parkinson’s disease patients develop different speech impairments that affect their communication capabilities. The automatic assessment of the speech of the patients allows the development of computer aided tools to support the diagnosis and the evaluation of the disease severity. This paper introduces a methodology to classify Parkinson’s disease from speech in three different languages: Spanish, German, and Czech. The proposed approach considers convolutional neural networks trained with time frequency representations and a transfer learning strategy among the three languages. The transfer learning scheme aims to improve the accuracy of the models when the weights of the neural network are initialized with utterances from a different language than the used for the test set. The results suggest that the proposed strategy improves the accuracy of the models in up to 8% when the base model used to initialize the weights of the classifier is robust enough. In addition, the results obtained after the transfer learning are in most cases more balanced in terms of specificity-sensitivity than those trained without the transfer learning strategy. Springer Nature Switzerland AG 2019.
Czech name
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Czech description
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Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISBN
9783030339036
ISSN
0302-9743
e-ISSN
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Number of pages
10
Pages from-to
697-706
Publisher name
Springer
Place of publication
Wien
Event location
Havana
Event date
Oct 28, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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