A Highly Accurate Dysphonia Detection System Using Linear Discriminant Analysis  

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

机构地区：[1]Department of Computer Engineering,Umm Al-Qura University,Makkah,Saudi Arabia [2]Department of Computer Science and Engineering,Khulna University of Engineering&Technology,Khulna,9203,Bangladesh

出　　处：《Computer Systems Science & Engineering》2023年第3期1921-1938,共18页计算机系统科学与工程（英文）

摘　　要：The recognition of pathological voice is considered a difficult task for speech analysis.Moreover,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 paper.We have leveraged Linear Discriminant Analysis(LDA)to train multiple Machine Learning(ML)models for dysphonia detection.Several 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 dataset.The K-fold cross-validation approach was incorporated to increase the robustness and stability of the ML models.According 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.

关 键 词：Dimensionality reduction dysphonia detection linear discriminant analysis logistic regression speech feature extraction support vector machine 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]