Detail publikace

Towards robust voice pathology detection Investigation of supervised deep learning, gradient boosting, and anomaly detection approaches across four databases

HARÁR, P. GALÁŽ, Z. ALONSO-HERNANDEZ, J. MEKYSKA, J. BURGET, R. SMÉKAL, Z.

Originální název

Towards robust voice pathology detection Investigation of supervised deep learning, gradient boosting, and anomaly detection approaches across four databases

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

voice patholgoy detection; deep learning; gradient boosting; anomaly detection

Autoři

HARÁR, P.; GALÁŽ, Z.; ALONSO-HERNANDEZ, J.; MEKYSKA, J.; BURGET, R.; SMÉKAL, Z.

Vydáno

2. 10. 2020

Nakladatel

Springer

ISSN

1433-3058

Periodikum

Neural Computing and Applications

Ročník

1

Číslo

1

Stát

Spojené království Velké Británie a Severního Irska

Strany od

15747

Strany do

15757

Strany počet

11

URL

https://link.springer.com/article/10.1007/s00521-018-3464-7

Plný text v Digitální knihovně

http://hdl.handle.net/11012/156794

BibTex

@article{BUT147134,
  author="Pavol {Harár} and Zoltán {Galáž} and Jesus {Alonso-Hernandez} and Jiří {Mekyska} and Radim {Burget} and Zdeněk {Smékal}",
  title="Towards robust voice pathology detection Investigation of supervised deep learning, gradient boosting, and anomaly detection approaches across four databases",
  journal="Neural Computing and Applications",
  year="2020",
  volume="1",
  number="1",
  pages="15747--15757",
  doi="10.1007/s00521-018-3464-7",
  issn="1433-3058",
  url="https://link.springer.com/article/10.1007/s00521-018-3464-7"
}