Biomedical Engineering Department Electrical Engineering Department, Ziauddin University Faculty of Engineering Science Technology and Management, Karachi, Pakistan.
Electrical Engineering Department & Software Engineering Department, Ziauddin University Faculty of Engineering Science Technology and Management, Karachi, Pakistan.
Math Biosci Eng. 2021 Mar 5;18(3):2258-2273. doi: 10.3934/mbe.2021114.
Voice pathologies are irregular vibrations produced due to vocal folds and various factors malfunctioning. In medical science, novel machine learning algorithms are applied to construct a system to identify disorders that occur invoice. This study aims to extract the features from the audio signals of four chosen diseases from the SVD dataset, such as laryngitis, cyst, non-fluency syndrome, and dysphonia, and then compare the four results of machine learning algorithms, i.e., SVM, Naïve Byes, decision tree and ensemble classifier. In this project, we have used a comparative approach along with the new combination of features to detect voice pathologies which are laryngitis, cyst, non-fluency syndrome, and dysphonia from the SVD dataset. The combination of specific 13 MFCC (mel-frequency cepstral coefficients) features along with pitch, zero crossing rate (ZCR), spectral flux, spectral entropy, spectral centroid, spectral roll-off, and short term energy for more accurate detection of voice pathologies. It is proven that the combination of features extracted gives the best product on the audio, which split into 10 ms. Four machine learning classifiers, SVM, Naïve Bayes, decision tree and ensemble classifier for the inter classifier comparison, give 93.18, 99.45,100 and 51%, respectively. Out of these accuracies, both Naïve Bayes and the decision tree show the most promising results with a higher detection rate. Naïve Bayes and decision tree gives the highest reported outcomes on the selected set of features in the proposed methodology. The SVM has also been concluded to be the commonly used voice condition identification algorithm.
嗓音障碍是由于声带和各种因素功能障碍而产生的不规则振动。在医学科学中，新颖的机器学习算法被应用于构建一个系统，以识别发票中发生的障碍。本研究旨在从 SVD 数据集的四种选定疾病（如喉炎、囊肿、非流畅综合征和发音障碍）的音频信号中提取特征，然后比较四种机器学习算法（即 SVM、朴素贝叶斯、决策树和集成分类器）的结果。在这个项目中，我们使用了一种比较方法，以及新的特征组合，从 SVD 数据集中检测出喉炎、囊肿、非流畅综合征和发音障碍等嗓音障碍。结合特定的 13 个 MFCC（梅尔频率倒谱系数）特征，以及音高、过零率（ZCR）、频谱通量、频谱熵、频谱质心、频谱滚降和短期能量，以更准确地检测嗓音障碍。事实证明，提取的特征组合在音频上产生了最佳的效果，该音频被分为 10ms。为了进行分类器间比较，使用了 SVM、朴素贝叶斯、决策树和集成分类器这四种机器学习分类器，它们的准确率分别为 93.18%、99.45%、100%和 51%。在这些准确率中，朴素贝叶斯和决策树表现出最有前途的结果，具有更高的检测率。朴素贝叶斯和决策树在提出的方法中对所选特征集给出了最高的报告结果。SVM 也被认为是常用的语音条件识别算法。
