A Parametric Approach for Classification of Distortions in Pathological Voices

Amir Hossein Poorjam, Max A Little, Jesper Rindom Jensen, Mads Græsbøll Christensen

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

In biomedical acoustics, distortion in voice signals, commonly present 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, namely 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 classifiers, namely support vector machines and probabilistic LDA classify 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 directly use acoustic features for distortion classification.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Pages286-290
ISBN (Electronic)978-1-5386-4658-8
ISBN (Print) 978-1-5386-4659-5
DOIs
Publication statusPublished - 13 Sep 2018
Event2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Calgary, Canada
Duration: 15 Apr 201820 Apr 2018

Publication series

Name2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
ISSN (Electronic)2379-190X

Conference

Conference2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
CountryCanada
CityCalgary
Period15/04/1820/04/18

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    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). (2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)). IEEE. https://doi.org/10.1109/ICASSP.2018.8461316