A Highly Accurate Dysphonia Detection System Using Linear Discriminant Analysis

作     者：Anas Basalamah Mahedi Hasan Shovan Bhowmik Shaikh Akib Shahriyar 

作者机构：Department of Computer EngineeringUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science and EngineeringKhulna University of Engineering&TechnologyKhulna9203Bangladesh 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第44卷第3期

页      面：1921-1938页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

主　　题：Dimensionality reduction dysphonia detection linear discriminant analysis logistic regression speech feature extraction support vector machine 

摘      要：The recognition of pathological voice is considered a difficult task for speech ***,otolaryngologists needed to rely on oral communication with patients to discover traces of voice pathologies like dysphonia that are caused by voice alteration of vocal folds and their accuracy is between 60%–70%.To enhance detection accuracy and reduce processing speed of dysphonia detection,a novel approach is proposed in this *** have leveraged Linear Discriminant Analysis(LDA)to train multiple Machine Learning(ML)models for dysphonia *** ML models are utilized like Support Vector Machine(SVM),Logistic Regression,and K-nearest neighbor(K-NN)to predict the voice pathologies based on features like Mel-Frequency Cepstral Coefficients(MFCC),Fundamental Frequency(F0),Shimmer(%),Jitter(%),and Harmonic to Noise Ratio(HNR).The experiments were performed using Saarbrucken Voice Data-base(SVD)and a privately collected *** K-fold cross-validation approach was incorporated to increase the robustness and stability of the ML *** to the experimental results,our proposed approach has a 70%increase in processing speed over Principal Component Analysis(PCA)and performs remarkably well with a recognition accuracy of 95.24%on the SVD dataset surpassing the previous best accuracy of 82.37%.In the case of the private dataset,our proposed method achieved an accuracy rate of 93.37%.It can be an effective non-invasive method to detect dysphonia.