Influence of delay time on regularity estimation for voice pathology detection

Godino Llorente, Juan Ignacio and Gómez García, J.A. and Castellanos Dominguez, G. (2012). Influence of delay time on regularity estimation for voice pathology detection. In: "34th Annual International Conference of the IEEE EMBS San Diego, California USA, 28 August - 1 September, 2012", 28/08/2012 al 1/09/2012, San Diego (California). ISBN 978-1-4577-1787-1.

Description

Title: Influence of delay time on regularity estimation for voice pathology detection
Author/s:
  • Godino Llorente, Juan Ignacio
  • Gómez García, J.A.
  • Castellanos Dominguez, G.
Item Type: Presentation at Congress or Conference (Article)
Event Title: 34th Annual International Conference of the IEEE EMBS San Diego, California USA, 28 August - 1 September, 2012
Event Dates: 28/08/2012 al 1/09/2012
Event Location: San Diego (California)
Title of Book: 34th Annual International Conference of the IEEE EMBS San Diego, California USA, 28 August - 1 September, 2012
Date: 2012
ISBN: 978-1-4577-1787-1
Subjects:
Faculty: E.U.I.T. Telecomunicación (UPM)
Department: Ingeniería de Circuitos y Sistemas [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The employment of nonlinear analysis techniques for automatic voice pathology detection systems has gained popularity due to the ability of such techniques for dealing with the underlying nonlinear phenomena. On this respect, characterization using nonlinear analysis typically employs the classical Correlation Dimension and the largest Lyapunov Exponent, as well as some regularity quantifiers computing the system predictability. Mostly, regularity features highly depend on a correct choosing of some parameters. One of those, the delay time �, is usually fixed to be 1. Nonetheless, it has been stated that a unity � can not avoid linear correlation of the time series and hence, may not correctly capture system nonlinearities. Therefore, present work studies the influence of the � parameter on the estimation of regularity features. Three � estimations are considered: the baseline value 1; a � based on the Average Automutual Information criterion; and � chosen from the embedding window. Testing results obtained for pathological voice suggest that an improved accuracy might be obtained by using a � value different from 1, as it accounts for the underlying nonlinearities of the voice signal.

More information

Item ID: 20301
DC Identifier: http://oa.upm.es/20301/
OAI Identifier: oai:oa.upm.es:20301
Deposited by: Memoria Investigacion
Deposited on: 14 Mar 2014 07:41
Last Modified: 21 Apr 2016 23:03
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