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Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms
المؤلفون المشاركون
Farahat, Mohamed
Muhammad, Ghulam
Ali, Zulfiqar
Alsulaiman, Mansour
Malki, Khalid H.
Al-nasheri, Ahmed
Mesallam, Tamer A.
المصدر
Journal of Healthcare Engineering
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-10-19
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1181287
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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