Voice pathology detection based on the modified voice contour and SVM

Zulfiqar Ali, Mansour Alsulaiman, Irraivan Elamvazuthi, Ghulam Muhammad, Tamer A. Mesallam, Mohamed Farahat, Khalid H. Malki

Research output: Contribution to journalLetter

11 Citations (Scopus)

Abstract

In this study, a novel method based on the voice intensity of a speech signal is used for automatic pathology detection with continuous speech. The proposed method determines the peaks from the speech signal to form a voice contour. The area under the voice contour allows us to discriminate between normal and disordered subjects. In the case of disordered subjects, the calculated area under the voice contour is lower than that for a normal subject due to the malfunctioning of vocal folds, which makes the voice weaker and breathier. Some long-term features such as shimmer and jitter are based on the accurate estimation of fundamental frequency, which is itself a difficult task, especially for disordered speech signals. The proposed feature does not need to estimate the pitch period or fundamental frequency during the calculation of the voice contour and they provide a single value for the whole utterance similar to other long-term features. The voice disorder database used in this study includes 71 normal subjects and the same number of disordered subjects. Each disordered subject has one of the following voice disorders: vocal folds cysts, laryngopharyngeal reflux disease, vocal folds polyps, unilateral vocal folds paralysis and sulcus vocalis. The accuracy of the proposed method is 100%.

LanguageEnglish
Pages10-18
Number of pages9
JournalBiologically Inspired Cognitive Architectures
Volume15
Early online date10 Nov 2015
Publication statusPublished - 31 Jan 2016

Fingerprint

Pathology
Vocal Cords
Voice Disorders
Laryngopharyngeal Reflux
Polyps
Paralysis
Cysts
Databases
Jitter

Keywords

  • Artificial intelligence
  • Continuous speech
  • Modified voice contour
  • Simpson's rule
  • Voice disorder detection

Cite this

Ali, Z., Alsulaiman, M., Elamvazuthi, I., Muhammad, G., Mesallam, T. A., Farahat, M., & Malki, K. H. (2016). Voice pathology detection based on the modified voice contour and SVM. Biologically Inspired Cognitive Architectures, 15, 10-18.
Ali, Zulfiqar ; Alsulaiman, Mansour ; Elamvazuthi, Irraivan ; Muhammad, Ghulam ; Mesallam, Tamer A. ; Farahat, Mohamed ; Malki, Khalid H. / Voice pathology detection based on the modified voice contour and SVM. In: Biologically Inspired Cognitive Architectures. 2016 ; Vol. 15. pp. 10-18.
@article{a054d116716a44559ecdc7932faa0d80,
title = "Voice pathology detection based on the modified voice contour and SVM",
abstract = "In this study, a novel method based on the voice intensity of a speech signal is used for automatic pathology detection with continuous speech. The proposed method determines the peaks from the speech signal to form a voice contour. The area under the voice contour allows us to discriminate between normal and disordered subjects. In the case of disordered subjects, the calculated area under the voice contour is lower than that for a normal subject due to the malfunctioning of vocal folds, which makes the voice weaker and breathier. Some long-term features such as shimmer and jitter are based on the accurate estimation of fundamental frequency, which is itself a difficult task, especially for disordered speech signals. The proposed feature does not need to estimate the pitch period or fundamental frequency during the calculation of the voice contour and they provide a single value for the whole utterance similar to other long-term features. The voice disorder database used in this study includes 71 normal subjects and the same number of disordered subjects. Each disordered subject has one of the following voice disorders: vocal folds cysts, laryngopharyngeal reflux disease, vocal folds polyps, unilateral vocal folds paralysis and sulcus vocalis. The accuracy of the proposed method is 100{\%}.",
keywords = "Artificial intelligence, Continuous speech, Modified voice contour, Simpson's rule, Voice disorder detection",
author = "Zulfiqar Ali and Mansour Alsulaiman and Irraivan Elamvazuthi and Ghulam Muhammad and Mesallam, {Tamer A.} and Mohamed Farahat and Malki, {Khalid H.}",
year = "2016",
month = "1",
day = "31",
language = "English",
volume = "15",
pages = "10--18",
journal = "Biologically Inspired Cognitive Architectures",
issn = "2212-683X",
publisher = "Elsevier",

}

Ali, Z, Alsulaiman, M, Elamvazuthi, I, Muhammad, G, Mesallam, TA, Farahat, M & Malki, KH 2016, 'Voice pathology detection based on the modified voice contour and SVM', Biologically Inspired Cognitive Architectures, vol. 15, pp. 10-18.

Voice pathology detection based on the modified voice contour and SVM. / Ali, Zulfiqar; Alsulaiman, Mansour; Elamvazuthi, Irraivan; Muhammad, Ghulam; Mesallam, Tamer A.; Farahat, Mohamed; Malki, Khalid H.

In: Biologically Inspired Cognitive Architectures, Vol. 15, 31.01.2016, p. 10-18.

Research output: Contribution to journalLetter

TY - JOUR

T1 - Voice pathology detection based on the modified voice contour and SVM

AU - Ali, Zulfiqar

AU - Alsulaiman, Mansour

AU - Elamvazuthi, Irraivan

AU - Muhammad, Ghulam

AU - Mesallam, Tamer A.

AU - Farahat, Mohamed

AU - Malki, Khalid H.

PY - 2016/1/31

Y1 - 2016/1/31

N2 - In this study, a novel method based on the voice intensity of a speech signal is used for automatic pathology detection with continuous speech. The proposed method determines the peaks from the speech signal to form a voice contour. The area under the voice contour allows us to discriminate between normal and disordered subjects. In the case of disordered subjects, the calculated area under the voice contour is lower than that for a normal subject due to the malfunctioning of vocal folds, which makes the voice weaker and breathier. Some long-term features such as shimmer and jitter are based on the accurate estimation of fundamental frequency, which is itself a difficult task, especially for disordered speech signals. The proposed feature does not need to estimate the pitch period or fundamental frequency during the calculation of the voice contour and they provide a single value for the whole utterance similar to other long-term features. The voice disorder database used in this study includes 71 normal subjects and the same number of disordered subjects. Each disordered subject has one of the following voice disorders: vocal folds cysts, laryngopharyngeal reflux disease, vocal folds polyps, unilateral vocal folds paralysis and sulcus vocalis. The accuracy of the proposed method is 100%.

AB - In this study, a novel method based on the voice intensity of a speech signal is used for automatic pathology detection with continuous speech. The proposed method determines the peaks from the speech signal to form a voice contour. The area under the voice contour allows us to discriminate between normal and disordered subjects. In the case of disordered subjects, the calculated area under the voice contour is lower than that for a normal subject due to the malfunctioning of vocal folds, which makes the voice weaker and breathier. Some long-term features such as shimmer and jitter are based on the accurate estimation of fundamental frequency, which is itself a difficult task, especially for disordered speech signals. The proposed feature does not need to estimate the pitch period or fundamental frequency during the calculation of the voice contour and they provide a single value for the whole utterance similar to other long-term features. The voice disorder database used in this study includes 71 normal subjects and the same number of disordered subjects. Each disordered subject has one of the following voice disorders: vocal folds cysts, laryngopharyngeal reflux disease, vocal folds polyps, unilateral vocal folds paralysis and sulcus vocalis. The accuracy of the proposed method is 100%.

KW - Artificial intelligence

KW - Continuous speech

KW - Modified voice contour

KW - Simpson's rule

KW - Voice disorder detection

UR - http://www.scopus.com/inward/record.url?scp=84957809880&partnerID=8YFLogxK

M3 - Letter

VL - 15

SP - 10

EP - 18

JO - Biologically Inspired Cognitive Architectures

T2 - Biologically Inspired Cognitive Architectures

JF - Biologically Inspired Cognitive Architectures

SN - 2212-683X

ER -

Ali Z, Alsulaiman M, Elamvazuthi I, Muhammad G, Mesallam TA, Farahat M et al. Voice pathology detection based on the modified voice contour and SVM. Biologically Inspired Cognitive Architectures. 2016 Jan 31;15:10-18.