Feature selection in pathological voice classification using dinamyc of component analysis

Sarria Paja, Milton Orlando and Daza Santacoloma, Genaro and Godino Llorente, Juan Ignacio and Castellanos Domínguez, César Germán and Sáenz Lechón, Nicolas (2008). Feature selection in pathological voice classification using dinamyc of component analysis. In: "4th International Symposium on Image/Video Communications (ISIVC'08)", 09/07/2008-11/07/2008, Deusto, España. ISBN 978-84-9830-164-9.

Description

Title: Feature selection in pathological voice classification using dinamyc of component analysis
Author/s:
  • Sarria Paja, Milton Orlando
  • Daza Santacoloma, Genaro
  • Godino Llorente, Juan Ignacio
  • Castellanos Domínguez, César Germán
  • Sáenz Lechón, Nicolas
Item Type: Presentation at Congress or Conference (Article)
Event Title: 4th International Symposium on Image/Video Communications (ISIVC'08)
Event Dates: 09/07/2008-11/07/2008
Event Location: Deusto, España
Title of Book: Proceedings of the 4th International Symposium on Image/Video Communications over Fixed and Mobile Networks
Date: 2008
ISBN: 978-84-9830-164-9
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

This paper presents a methodology for the reduction of the training space based on the analysis of the variation of the linear components of the acoustic features. The methodology is applied to the automatic detection of voice disorders by means of stochastic dynamic models. The acoustic features used to model the speech are: MFCC, HNR, GNE, NNE and the energy envelopes. The feature extraction is carried out by means of PCA, and classification is done using discrete and continuous HMMs. The results showed a direct relationship between the principal directions (feature weights) and the classification performance. The dynamic feature analysis by means of PCA reduces the dimension of the original feature space while the topological complexity of the dynamic classifier remains unchanged. The experiments were tested with Kay Elemetrics (DB1) and UPM (DB2) databases. Results showed 91% of accuracy with 30% of computational cost reduction for DB1.

More information

Item ID: 3401
DC Identifier: http://oa.upm.es/3401/
OAI Identifier: oai:oa.upm.es:3401
Official URL: http://www.isivc2008.deusto.es/index.php?option=com_frontpage&Itemid=1
Deposited by: Memoria Investigacion
Deposited on: 22 Jun 2010 11:19
Last Modified: 20 Apr 2016 12:56
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