Literature DB >> 10757697

Identification of pathological voices using glottal noise measures.

V Parsa1, D G Jamieson.   

Abstract

We investigated the abilities of four fundamental frequency (F0)-dependent and two F0-independent measures to quantify vocal noise. Two of the F0-dependent measures were computed in the time domain, and two were computed using spectral information from the vowel. The F0-independent measures were based on the linear prediction (LP) modeling of vowel samples. Tests using a database of sustained vowel samples, collected from 53 normal and 175 pathological talkers, showed that measures based on the LP model were much superior to the other measures. A classification rate of 96.5% was achieved by a parameter that quantifies the spectral flatness of the unmodeled component of the vowel sample.

Entities:  

Mesh:

Year:  2000        PMID: 10757697     DOI: 10.1044/jslhr.4302.469

Source DB:  PubMed          Journal:  J Speech Lang Hear Res        ISSN: 1092-4388            Impact factor:   2.297


  14 in total

1.  Acoustic analysis of voice using WPCVox: a comparative study with Multi Dimensional Voice Program.

Authors:  Juan Ignacio Godino-Llorente; Víctor Osma-Ruiz; Nicolás Sáenz-Lechón; Ignacio Cobeta-Marco; Ramón González-Herranz; Carlos Ramírez-Calvo
Journal:  Eur Arch Otorhinolaryngol       Date:  2007-10-09       Impact factor: 2.503

2.  The Effects of Speech Compression Algorithms on the Intelligibility of Two Individuals With Dysarthric Speech.

Authors:  Rene L Utianski; Steven Sandoval; Visar Berisha; Kaitlin L Lansford; Julie M Liss
Journal:  Am J Speech Lang Pathol       Date:  2019-02-21       Impact factor: 2.408

3.  Pathological speech signal analysis and classification using empirical mode decomposition.

Authors:  Muhammad Kaleem; Behnaz Ghoraani; Aziz Guergachi; Sridhar Krishnan
Journal:  Med Biol Eng Comput       Date:  2013-03-05       Impact factor: 2.602

4.  Feature analysis of pathological speech signals using local discriminant bases technique.

Authors:  K Umapathy; S Krishnan
Journal:  Med Biol Eng Comput       Date:  2005-07       Impact factor: 2.602

5.  Articulation error of children with adenoid hypertrophy.

Authors:  Tae-Hoon Eom; Eun-Sil Jang; Young-Hoon Kim; Seung-Yun Chung; In-Goo Lee
Journal:  Korean J Pediatr       Date:  2014-07-23

6.  Robustness of auditory Teager Energy Cepstrum Coefficients for classification of pathological and normal voices in noisy environments.

Authors:  Lotfi Salhi; Adnane Cherif
Journal:  ScientificWorldJournal       Date:  2013-05-28

7.  An investigation of vocal tract characteristics for acoustic discrimination of pathological voices.

Authors:  Jung-Won Lee; Hong-Goo Kang; Jeung-Yoon Choi; Young-Ik Son
Journal:  Biomed Res Int       Date:  2013-10-31       Impact factor: 3.411

8.  Modulation Spectra Morphological Parameters: A New Method to Assess Voice Pathologies according to the GRBAS Scale.

Authors:  Laureano Moro-Velázquez; Jorge Andrés Gómez-García; Juan Ignacio Godino-Llorente; Gustavo Andrade-Miranda
Journal:  Biomed Res Int       Date:  2015-10-18       Impact factor: 3.411

9.  Using Ambulatory Voice Monitoring to Investigate Common Voice Disorders: Research Update.

Authors:  Daryush D Mehta; Jarrad H Van Stan; Matías Zañartu; Marzyeh Ghassemi; John V Guttag; Víctor M Espinoza; Juan P Cortés; Harold A Cheyne; Robert E Hillman
Journal:  Front Bioeng Biotechnol       Date:  2015-10-16

10.  Voice Pathology Detection Using Modulation Spectrum-Optimized Metrics.

Authors:  Laureano Moro-Velázquez; Jorge Andrés Gómez-García; Juan Ignacio Godino-Llorente
Journal:  Front Bioeng Biotechnol       Date:  2016-01-20
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.