Literature DB >> 19668892

Acoustic analysis of normal Saudi adult voices.

Khalid H Malki1, Salman F Al-Habib, Abulrahman A Hagr, Mohamed M Farahat.   

Abstract

OBJECTIVE: To determine the acoustic differences between Saudi adult male and female voices, and to compare the acoustic variables of the Multidimensional Voice Program (MDVP) obtained from North American adults to a group of Saudi males and females.
METHODS: A cross-sectional survey of normal adult male and female voices was conducted at King Abdulaziz University Hospital, Riyadh, Kingdom of Saudi Arabia between March 2007 and December 2008. Ninety-five Saudi subjects sustained the vowel /a/ 6 times, and the steady state portion of 3 samples was analyzed and compared with the samples of the KayPentax normative voice database.
RESULTS: Significant differences were found between Saudi and North American KayPentax database groups. In the male subjects, 15 of 33 MDVP variables, and 10 of 33 variables in the female subjects were found to be significantly different from the KayPentax database.
CONCLUSION: We conclude that the acoustical differences may reflect laryngeal anatomical or tissue differences between the Saudi and the KayPentax database.

Mesh:

Year:  2009        PMID: 19668892

Source DB:  PubMed          Journal:  Saudi Med J        ISSN: 0379-5284            Impact factor:   1.484


  3 in total

1.  Validation and cultural modification of Arabic voice handicap index.

Authors:  Khalid H Malki; Tamer A Mesallam; Mohamed Farahat; Manal Bukhari; Thomas Murry
Journal:  Eur Arch Otorhinolaryngol       Date:  2010-06-09       Impact factor: 2.503

2.  The effect of cochlear implantation and post-operative rehabilitation on acoustic voice analysis in post-lingual hearing impaired adults.

Authors:  Sabah M Hassan; Khalid H Malki; Tamer A Mesallam; Mohamad Farahat; Manal Bukhari; Thomas Murry
Journal:  Eur Arch Otorhinolaryngol       Date:  2011-02-18       Impact factor: 2.503

3.  Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms.

Authors:  Tamer A Mesallam; Mohamed Farahat; Khalid H Malki; Mansour Alsulaiman; Zulfiqar Ali; Ahmed Al-Nasheri; Ghulam Muhammad
Journal:  J Healthc Eng       Date:  2017-10-19       Impact factor: 2.682

  3 in total

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