Comparative Analysis of Pitch Detection Algorithms for Machine Learning Supported Parkinson’s Disease Diagnosis

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

Comparative Analysis of Pitch Detection Algorithms for Machine Learning Supported Parkinson’s Disease Diagnosis

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Comparative Analysis of Pitch Detection Algorithms for Machine Learning Supported Parkinson’s Disease Diagnosis

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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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Počet stran výsledku

6

Strana od-do

105-110

Název nakladatele

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Místo vydání

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Místo konání akce

Brno

Datum konání akce

29. 4. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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