Biomedical Engineering Department, Ziauddin University Faculty of Engineering Science Technology and Management, Karachi, Pakistan.
Software Engineering Department, Ziauddin University Faculty of Engineering Science Technology and Management, Karachi, Pakistan.
Math Biosci Eng. 2020 Nov 11;17(6):7958-7979. doi: 10.3934/mbe.2020404.
Voice disorders are pathological conditions that directly affect voice production. Computer based diagnosis may play a major role in the early detection and in tracking and even development of efficient pathological speech diagnosis, based on a computerized acoustic evaluation. The health of the Voice is assessed by several acoustic parameters. The exactness of these parameters is often linked to algorithms used to estimate them for speech noise identification. That is why main effort of the scientists is to study acoustic parameters and to apply classification methods that achieve a high precision in discrimination. The primary aim of this paper is for a meta-analysis on voice disorder databases i.e. SVD, MEEI and AVPD and machine learning techniques applied on it. This field of study was systematically reviewed in compliance with PRISMA guidelines. A search was performed with a set of formulated keywords on three databases i.e. Science Direct, PubMed, and IEEE Xplore. A proper screening and analysis of articles were performed after which several articles were also excluded. Forty-five studies that fulfills the eligibility criteria were included in this meta-analysis. After applying eligibility criteria on the peer reviewed and research article and studies that were published in authentic journals and conferences proceedings till June 2020 were chosen for further full-text screening. In general, only those articles that used voice recordings from SVD, MEEI and AVPD databases as a dataset is included in this meta-analysis. : We discussed the strengths and weaknesses of SVD, MEEI and AVPD. After detailed analysis of the studies including the techniques used and outcome measurements, it was also concluded that Support Vector Machine (SVM) is the most common used algorithm for the detection of voice disorders. Other than was also noticed that researchers focus on supervised techniques for the clinical diagnosis of voice disorder rather than using unsupervised techniques. It was also concluded that more work needs to be on voice pathology detection using AVPD database.
嗓音障碍是直接影响发声的病理性状况。基于计算机的声学评估，计算机诊断可能在早期检测以及跟踪，甚至发展高效的病理性语音诊断方面发挥重要作用。嗓音健康通过几个声学参数来评估。这些参数的准确性通常与用于估计语音噪声识别的算法有关。这就是为什么科学家们的主要努力是研究声学参数并应用分类方法，以实现高准确率的区分。本文的主要目的是对嗓音障碍数据库（即 SVD、MEEI 和 AVPD）进行元分析，并对其应用机器学习技术。 本研究领域是根据 PRISMA 指南进行系统回顾的。在三个数据库（Science Direct、PubMed 和 IEEE Xplore）中使用一组制定的关键词进行了搜索。在适当筛选和分析文章后，还排除了一些文章。共有 45 项符合入选标准的研究被纳入本元分析。在对同行评议和研究文章以及发表在真实期刊和会议论文集上的文章进行入选标准应用和研究后，选择进一步进行全文筛选。一般来说，只有那些使用 SVD、MEEI 和 AVPD 数据库中的嗓音录音作为数据集的文章才包括在本元分析中。 ：我们讨论了 SVD、MEEI 和 AVPD 的优缺点。在详细分析包括使用的技术和结果测量的研究之后，还得出结论，支持向量机（SVM）是用于检测嗓音障碍的最常用算法。此外，还注意到研究人员更关注用于嗓音障碍临床诊断的监督技术，而不是使用无监督技术。还得出结论，需要在使用 AVPD 数据库进行嗓音病理检测方面做更多工作。
