TY - GEN
T1 - A Parametric Approach for Classification of Distortions in Pathological Voices
AU - Poorjam, Amir Hossein
AU - Little, Max A
AU - Jensen, Jesper Rindom
AU - Christensen, Mads Græsbøll
N1 - © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
PY - 2018/9/13
Y1 - 2018/9/13
N2 - 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.
AB - 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.
UR - https://ieeexplore.ieee.org/document/8461316/?tp=&arnumber=8461316&contentType=Conferences&dld=YXN0b24uYWMudWs%3D&source=SEARCHALERT
U2 - 10.1109/ICASSP.2018.8461316
DO - 10.1109/ICASSP.2018.8461316
M3 - Conference publication
SN - 978-1-5386-4659-5
T3 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
SP - 286
EP - 290
BT - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PB - IEEE
T2 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Y2 - 15 April 2018 through 20 April 2018
ER -