An Investigation of Multidimensional Voice Program Parameters in Three Different Databases for Voice Pathology Detection and Classification

Ahmed Al-nasheri, Ghulam Muhammad, Mansour Alsulaiman, Zulfiqar Ali, Tamer A. Mesallam, Mohamed Farahat, Khalid H. Malki, Mohamed A. Bencherif

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

Background and Objective Automatic voice-pathology detection and classification systems may help clinicians to detect the existence of any voice pathologies and the type of pathology from which patients suffer in the early stages. The main aim of this paper is to investigate Multidimensional Voice Program (MDVP) parameters to automatically detect and classify the voice pathologies in multiple databases, and then to find out which parameters performed well in these two processes. Materials and Methods Samples of the sustained vowel /a/ of normal and pathological voices were extracted from three different databases, which have three voice pathologies in common. The selected databases in this study represent three distinct languages: (1) the Arabic voice pathology database; (2) the Massachusetts Eye and Ear Infirmary database (English database); and (3) the Saarbruecken Voice Database (German database). A computerized speech lab program was used to extract MDVP parameters as features, and an acoustical analysis was performed. The Fisher discrimination ratio was applied to rank the parameters. A t test was performed to highlight any significant differences in the means of the normal and pathological samples. Results The experimental results demonstrate a clear difference in the performance of the MDVP parameters using these databases. The highly ranked parameters also differed from one database to another. The best accuracies were obtained by using the three highest ranked MDVP parameters arranged according to the Fisher discrimination ratio: these accuracies were 99.68%, 88.21%, and 72.53% for the Saarbruecken Voice Database, the Massachusetts Eye and Ear Infirmary database, and the Arabic voice pathology database, respectively.

LanguageEnglish
Pages113.e9-113.e18
Number of pages10
JournalJournal of Voice
Volume31
Issue number1
Early online date19 Apr 2016
Publication statusPublished - 31 Jan 2017

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Databases
Pathology
Ear
Language

Keywords

  • AVPD
  • MDVP parameters
  • MEEI
  • SVD
  • SVM

Cite this

Al-nasheri, A., Muhammad, G., Alsulaiman, M., Ali, Z., Mesallam, T. A., Farahat, M., ... Bencherif, M. A. (2017). An Investigation of Multidimensional Voice Program Parameters in Three Different Databases for Voice Pathology Detection and Classification. Journal of Voice, 31(1), 113.e9-113.e18.
Al-nasheri, Ahmed ; Muhammad, Ghulam ; Alsulaiman, Mansour ; Ali, Zulfiqar ; Mesallam, Tamer A. ; Farahat, Mohamed ; Malki, Khalid H. ; Bencherif, Mohamed A. / An Investigation of Multidimensional Voice Program Parameters in Three Different Databases for Voice Pathology Detection and Classification. In: Journal of Voice. 2017 ; Vol. 31, No. 1. pp. 113.e9-113.e18.
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Al-nasheri, A, Muhammad, G, Alsulaiman, M, Ali, Z, Mesallam, TA, Farahat, M, Malki, KH & Bencherif, MA 2017, 'An Investigation of Multidimensional Voice Program Parameters in Three Different Databases for Voice Pathology Detection and Classification', Journal of Voice, vol. 31, no. 1, pp. 113.e9-113.e18.

An Investigation of Multidimensional Voice Program Parameters in Three Different Databases for Voice Pathology Detection and Classification. / Al-nasheri, Ahmed; Muhammad, Ghulam; Alsulaiman, Mansour; Ali, Zulfiqar; Mesallam, Tamer A.; Farahat, Mohamed; Malki, Khalid H.; Bencherif, Mohamed A.

In: Journal of Voice, Vol. 31, No. 1, 31.01.2017, p. 113.e9-113.e18.

Research output: Contribution to journalArticle

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AU - Mesallam, Tamer A.

AU - Farahat, Mohamed

AU - Malki, Khalid H.

AU - Bencherif, Mohamed A.

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N2 - Background and Objective Automatic voice-pathology detection and classification systems may help clinicians to detect the existence of any voice pathologies and the type of pathology from which patients suffer in the early stages. The main aim of this paper is to investigate Multidimensional Voice Program (MDVP) parameters to automatically detect and classify the voice pathologies in multiple databases, and then to find out which parameters performed well in these two processes. Materials and Methods Samples of the sustained vowel /a/ of normal and pathological voices were extracted from three different databases, which have three voice pathologies in common. The selected databases in this study represent three distinct languages: (1) the Arabic voice pathology database; (2) the Massachusetts Eye and Ear Infirmary database (English database); and (3) the Saarbruecken Voice Database (German database). A computerized speech lab program was used to extract MDVP parameters as features, and an acoustical analysis was performed. The Fisher discrimination ratio was applied to rank the parameters. A t test was performed to highlight any significant differences in the means of the normal and pathological samples. Results The experimental results demonstrate a clear difference in the performance of the MDVP parameters using these databases. The highly ranked parameters also differed from one database to another. The best accuracies were obtained by using the three highest ranked MDVP parameters arranged according to the Fisher discrimination ratio: these accuracies were 99.68%, 88.21%, and 72.53% for the Saarbruecken Voice Database, the Massachusetts Eye and Ear Infirmary database, and the Arabic voice pathology database, respectively.

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