Acoustic analysis of the unvoiced stop consonants for detecting hypernasal speech

Castellanos Domínguez, César Germán and Sepúlveda Sepúlveda, Franklin Alexander and Godino Llorente, Juan Ignacio (2008). Acoustic analysis of the unvoiced stop consonants for detecting hypernasal speech. In: "4th International Symposium on Image/Video Communications (ISIVC'08)", 09/07/2008-11/07/2008, Bilbao, España. ISBN 978-84-9830-164-9.

Description

Title: Acoustic analysis of the unvoiced stop consonants for detecting hypernasal speech
Author/s:
  • Castellanos Domínguez, César Germán
  • Sepúlveda Sepúlveda, Franklin Alexander
  • Godino Llorente, Juan Ignacio
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: Bilbao, 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

Speakers having evidence of a defective velopharyngeal mechanism produce speech with inappropriate nasal resonance (hypernasal speech). Voice analysis methods for the detection of hypernasality commonly use vowels and nasalized vowels. However, to obtain a more general assessment of this abnormality it is necessary to analyze stops and fricatives. This study describes a method for hipernasality detection analyzing the unvoiced Spanish stop consonants /k/ and /p/, as well. The importance of phonemeby- phoneme analysis is shown, in contrast with whole word parametrization which may include irrelevant segments from the classification point of view. Parameters that correlate the imprints of Velopharyngeal Incompetence (VPI) over voiceless stop consonants were used in the feature estimation stage. Classification was carried out using a Support Vector Machine (SVM), obtaining a performance of 74% for a repeated cross-validation strategy evaluation.

More information

Item ID: 3402
DC Identifier: http://oa.upm.es/3402/
OAI Identifier: oai:oa.upm.es:3402
Official URL: http://www.isivc2008.deusto.es/index.php?option=com_frontpage&Itemid=1
Deposited by: Memoria Investigacion
Deposited on: 22 Jun 2010 11:05
Last Modified: 20 Apr 2016 12:56
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