NDLI: Automatic assessment of voice quality in the context of multiple annotations

Content Provider	IEEE Xplore Digital Library
Author	Gil Gonzalez, J. Alvarez, M.A. Orozco, A.A.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. Eng., Univ. Tecnol. de Pereira, Pereira, Colombia (Gil Gonzalez, J.; Alvarez, M.A.; Orozco, A.A.)
Abstract	Approaches to evaluate voice quality include perceptual analysis, and acoustical analysis. Perceptual analysis is subjective and depends mostly on the ability of a specialist to assess a pathology, whereas acoustical analysis is objective, but highly relies on the quality of the so called annotations that the specialist assigns to the voice signal. The quality of the annotations for acoustical analysis depends heavily on the expertise and knowledge of the specialist. We face a scenario where we have annotations performed by several specialists with different levels of expertise and knowledge. Traditional pattern recognition methods employed in acoustical analysis are no longer applicable, since these methods are designed for scenarios where a “ground-truth” label is assigned by the specialist. In this paper, we apply recent developments in machine learning for taking into account multiple annotators for acoustical analysis of voice signals. For the classification step we compare two techniques, one of them based on Gaussian Processes for regression with multiple annotators, and the other is a multi-class Logistic Regression model that measures the annotator performance in terms of sensitivity and specificity. The performance of classifiers is assessed in terms of Cohen's Kappa index. Results show that the multi-annotator classification schemes have better performance when compared to techniques based on a traditional classifier where the true label is estimated from the multiple annotations available using majority voting.
Starting Page	6236
Ending Page	6239
File Size	584527
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7319817
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mel frequency cepstral coefficient Indexes Pathology Gaussian processes Logistics Mathematical model Diseases
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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