Comparative Analysis of Pitch Detection Algorithms for Machine Learning Supported Parkinson’s Disease Diagnosis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0198684" target="_blank" >RIV/00216305:26220/26:0198684 - isvavai.cz</a>
Result on the web
<a href="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2025_sbornik_1.pdf" target="_blank" >https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2025_sbornik_1.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Comparative Analysis of Pitch Detection Algorithms for Machine Learning Supported Parkinson’s Disease Diagnosis
Original language description
Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms, including hypokinetic dysarthria (HD), a speech disorder affecting prosody. Early detection of PD through speech analysis offers a promising, non-invasive diagnostic approach. This study evaluates five pitch detection algorithms—PRAAT, YIN, PYIN, RAPT, and SWIPE’—to extract fundamental frequency-based features from the PARCZ speech database. The extracted features, including relative F0, standard deviation and various jitter measures, are used to train and evaluate three binary classifiers: Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF). The classifiers are optimized using a stratified crossvalidation approach, with balanced accuracy as the primary metric. Results indicate that while pitch-based features alone are insufficient for clinically accurate PD diagnosis, certain classifiers and pitch detection methods show potential in aiding early detection. Future work should incorporate a broader set of speech parameters to enhance diagnostic precision. © 2025, Brno University of Technology. All rights reserved.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings I of the 31st Conference STUDENT EEICT 2025
ISBN
978-80-214-6321-9
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
105-110
Publisher name
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Place of publication
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Event location
Brno
Event date
Apr 29, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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