A Parametric Approach for Classification of Distortions in Pathological Voices

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Abstract

In biomedical acoustics, distortion in voice signals, commonly pre- sent during acquisition and transmission, adversely affects acoustic features extracted from pathological voice. Information on the type of distortion can help in compensating for its effects. This paper proposes a new approach to detecting four major types of commonly encountered distortion in remote analysis of pathological voice, na- mely background noise, reverberation, clipping and coding. In this approach, by applying factor analysis to Gaussian mixture model mean supervectors, distortions in variable-duration recordings are modeled by fixed-length, low-dimensional channel vectors. Then, linear discriminant analysis (LDA) is used to remove the remaining nuisance effects in the channel vectors. Finally, two different clas- sifiers, namely support vector machines and probabilistic LDA clas- sify the different types of distortion. Experimental results obtained using Parkinson’s voices, as an example of pathological voice, show 11.4% relative improvement in performance over systems which di- rectly use acoustic features for distortion classification.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Number of pages5
PublisherIEEE
Publication date10 Sep 2018
Pages286-290
Article number8461316
ISBN (Print)978-1-5386-4659-5
ISBN (Electronic)978-1-5386-4658-8
DOIs
Publication statusPublished - 10 Sep 2018
Event2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Calgary, Canada
Duration: 15 Apr 201820 Apr 2018
https://2018.ieeeicassp.org/

Conference

Conference2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
CountryCanada
CityCalgary
Period15/04/201820/04/2018
Internet address
SeriesI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149

Fingerprint

Discriminant analysis
Acoustic distortion
Acoustics
Reverberation
Factor analysis
Acoustic noise
Support vector machines
Classifiers

Keywords

  • Channel factors
  • Distortion modeling
  • PLDA
  • Remote pathological voice analysis
  • SVM

Cite this

Poorjam, A. H., Little, M. A., Jensen, J. R., & Christensen, M. G. (2018). A Parametric Approach for Classification of Distortions in Pathological Voices. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 286-290). [8461316] IEEE. I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings https://doi.org/10.1109/ICASSP.2018.8461316
Poorjam, Amir Hossein ; Little, Max A ; Jensen, Jesper Rindom ; Christensen, Mads Græsbøll. / A Parametric Approach for Classification of Distortions in Pathological Voices. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. pp. 286-290 (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings).
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Poorjam, AH, Little, MA, Jensen, JR & Christensen, MG 2018, A Parametric Approach for Classification of Distortions in Pathological Voices. in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)., 8461316, IEEE, I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings, pp. 286-290, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, 15/04/2018. https://doi.org/10.1109/ICASSP.2018.8461316

A Parametric Approach for Classification of Distortions in Pathological Voices. / Poorjam, Amir Hossein; Little, Max A; Jensen, Jesper Rindom; Christensen, Mads Græsbøll.

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. p. 286-290 8461316.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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Poorjam AH, Little MA, Jensen JR, Christensen MG. A Parametric Approach for Classification of Distortions in Pathological Voices. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2018. p. 286-290. 8461316. (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings). https://doi.org/10.1109/ICASSP.2018.8461316