NDLI: Automatic Detection of Pathological Voices Using Complexity Measures, Noise Parameters, and Mel-Cepstral Coefficients

Content Provider	IEEE Xplore Digital Library
Author	Arias-Londoño, J.D. Godino-Llorente, J.I. Sáenz-Lechón, N. Osma-Ruiz, V. Castellanos-Domínguez, G.
Copyright Year	1964
Abstract	This paper proposes a new approach to improve the amount of information extracted from the speech aiming to increase the accuracy of a system developed for the automatic detection of pathological voices. The paper addresses the discrimination capabilities of 11 features extracted using nonlinear analysis of time series. Two of these features are based on conventional nonlinear statistics (largest Lyapunov exponent and correlation dimension), two are based on recurrence and fractal-scaling analysis, and the remaining are based on different estimations of the entropy. Moreover, this paper uses a strategy based on combining classifiers for fusing the nonlinear analysis with the information provided by classic parameterization approaches found in the literature (noise parameters and mel-frequency cepstral coefficients). The classification was carried out in two steps using, first, a generative and, later, a discriminative approach. Combining both classifiers, the best accuracy obtained is 98.23% ± 0.001.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	10
File Size	515988
Starting Page	370
Ending Page	379
File Format	PDF
ISSN	00189294
Volume Number	58
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Entropy Pathology Speech Complexity theory Noise Accuracy Trajectory support vector machines (SVMs) Combining classifiers Gaussian mixture models (GMMs) nonlinear analysis pathological voices
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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