ENT Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
Digital Speech Processing Group, Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
J Healthc Eng. 2017;2017:8783751. doi: 10.1155/2017/8783751. Epub 2017 Oct 19.
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.
嗓音障碍数据库是进行自动嗓音障碍检测和分类研究的重要组成部分。种族会影响一个人的嗓音特征，因此有必要通过收集目标种族的嗓音样本来开发数据库。这将通过了解当地群体的特征，增加准确可靠地诊断嗓音障碍的全球解决方案的机会。受此启发，本研究设计并开发了一个阿拉伯语嗓音病理学数据库（AVPD），通过录制三个元音、朗读和孤立词来实现。对于录制的每个样本，还提供了感知严重程度，这是 AVPD 的一个独特方面。在开发 AVPD 的过程中，我们识别了不同嗓音障碍数据库的缺点，以便在 AVPD 中避免这些缺点。此外，我们还使用了六种不同类型的语音特征和四种类型的机器学习算法来评估 AVPD。使用持续元音和朗读语音获得的嗓音障碍检测和分类结果也与英语障碍数据库，即马萨诸塞州眼耳医院（MEEI）数据库的结果进行了比较。
