Towards Robust Voice Pathology Detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F19%3APU127744" target="_blank" >RIV/00216305:26220/19:PU127744 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s00521-018-3464-7" target="_blank" >https://link.springer.com/article/10.1007/s00521-018-3464-7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00521-018-3464-7" target="_blank" >10.1007/s00521-018-3464-7</a>
Alternative languages
Result language
angličtina
Original language name
Towards Robust Voice Pathology Detection
Original language description
Automatic objective non-invasive detection of pathological voice based on computerized analysis of acoustic signals can play an important role in early diagnosis, progression tracking and even effective treatment of pathological voices. In search towards such a robust voice pathology detection system we investigated 3 distinct classifiers within supervised learning and anomaly detection paradigms. We conducted a set of experiments using a variety of input data such as raw waveforms, spectrograms, mel-frequency cepstral coefficients (MFCC) and conventional acoustic (dysphonic) features (AF). In comparison with previously published works, this article is the first to utilize combination of 4 different databases comprising normophonic and pathological recordings of sustained phonation of the vowel /a/ unrestricted to a subset of vocal pathologies. Furthermore, to our best knowledge, this article is the first to explore gradient boosted trees and deep learning for this application. The following best classification performances measured by F1 score on dedicated test set were achieved: XGBoost (0.733) using AF and MFCC, DenseNet (0.621) using MFCC, and Isolation Forest (0.610) using AF. Even though these results are of exploratory character, conducted experiments do show promising potential of gradient boosting and deep learning methods to robustly detect voice pathologies.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neural Computing and Applications
ISSN
0941-0643
e-ISSN
1433-3058
Volume of the periodical
1
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
11
Pages from-to
1-11
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85044933261