Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms
Joint Authors
Farahat, Mohamed
Muhammad, Ghulam
Ali, Zulfiqar
Alsulaiman, Mansour
Malki, Khalid H.
Al-nasheri, Ahmed
Mesallam, Tamer A.
Source
Journal of Healthcare Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-19
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
A voice disorder database is an essential element in doing research on automatic voice disorder detection and classification.
Ethnicity affects the voice characteristics of a person, and so it is necessary to develop a database by collecting the voice samples of the targeted ethnic group.
This will enhance the chances of arriving at a global solution for the accurate and reliable diagnosis of voice disorders by understanding the characteristics of a local group.
Motivated by such idea, an Arabic voice pathology database (AVPD) is designed and developed in this study by recording three vowels, running speech, and isolated words.
For each recorded samples, the perceptual severity is also provided which is a unique aspect of the AVPD.
During the development of the AVPD, the shortcomings of different voice disorder databases were identified so that they could be avoided in the AVPD.
In addition, the AVPD is evaluated by using six different types of speech features and four types of machine learning algorithms.
The results of detection and classification of voice disorders obtained with the sustained vowel and the running speech are also compared with the results of an English-language disorder database, the Massachusetts Eye and Ear Infirmary (MEEI) database.
American Psychological Association (APA)
Mesallam, Tamer A.& Farahat, Mohamed& Malki, Khalid H.& Alsulaiman, Mansour& Ali, Zulfiqar& Al-nasheri, Ahmed…[et al.]. 2017. Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms. Journal of Healthcare Engineering،Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1181287
Modern Language Association (MLA)
Mesallam, Tamer A.…[et al.]. Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms. Journal of Healthcare Engineering No. 2017 (2017), pp.1-13.
https://search.emarefa.net/detail/BIM-1181287
American Medical Association (AMA)
Mesallam, Tamer A.& Farahat, Mohamed& Malki, Khalid H.& Alsulaiman, Mansour& Ali, Zulfiqar& Al-nasheri, Ahmed…[et al.]. Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms. Journal of Healthcare Engineering. 2017. Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1181287
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1181287