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Neural Computing and Applications
Published in: Neural Computing and Applications 20/2020

04-04-2018 | S.I.: Advances in Bio-Inspired Intelligent Systems

Towards robust voice pathology detection

Investigation of supervised deep learning, gradient boosting, and anomaly detection approaches across four databases

Authors: Pavol Harar, Zoltan Galaz, Jesus B. Alonso-Hernandez, Jiri Mekyska, Radim Burget, Zdenek Smekal

Published in: Neural Computing and Applications | Issue 20/2020

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Abstract

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 three 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 four 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.
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Metadata
Title
Towards robust voice pathology detection
Investigation of supervised deep learning, gradient boosting, and anomaly detection approaches across four databases
Authors
Pavol Harar
Zoltan Galaz
Jesus B. Alonso-Hernandez
Jiri Mekyska
Radim Burget
Zdenek Smekal
Publication date
04-04-2018
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 20/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3464-7

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