Federated Learning for Secure Development of AI Models for Parkinson’s Disease Detection Using Speech from Different Languages

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00368522" target="_blank" >RIV/68407700:21230/23:00368522 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.21437/Interspeech.2023-2108" target="_blank" >https://doi.org/10.21437/Interspeech.2023-2108</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.21437/Interspeech.2023-2108" target="_blank" >10.21437/Interspeech.2023-2108</a>

Result language
angličtina
Original language name
Federated Learning for Secure Development of AI Models for Parkinson’s Disease Detection Using Speech from Different Languages
Original language description
Parkinson's disease (PD) is a neurological disorder impacting a person's speech. Among automatic PD assessment methods, deep learning models have gained particular interest. Recently, the community has explored cross-pathology and cross-language models which can improve diagnostic accuracy even further. However, strict patient data privacy regulations largely prevent institutions from sharing patient speech data with each other. In this paper, we employ federated learning (FL) for PD detection using speech signals from 3 real-world language corpora of German, Spanish, and Czech, each from a separate institution. Our results indicate that the FL model outperforms all the local models in terms of diagnostic accuracy, while not performing very differently from the model based on centrally combined training sets, with the advantage of not requiring any data sharing among collaborators. This will simplify inter-institutional collaborations, resulting in enhancement of patient outcomes.
Czech name
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Czech description
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Project
<a href="/en/project/LX22NPO5107" target="_blank" >LX22NPO5107: National institute for Neurological Research</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2023
ISBN
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ISSN
2308-457X
e-ISSN
—
Number of pages
5
Pages from-to
5003-5007
Publisher name
ISCA - International Speech Communication Association
Place of publication
Bochum
Event location
Dublin
Event date
Aug 21, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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