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A Highly Accurate Dysphonia Detection System Using Linear Discriminant Analysis

Anas Basalamah1, Mahedi Hasan2, Shovan Bhowmik2, Shaikh Akib Shahriyar2,*
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
* Corresponding Author: Shaikh Akib Shahriyar. Email:

Computer Systems Science and Engineering 2023, 44(3), 1921-1938. https://doi.org/10.32604/csse.2023.027399

Received 17 January 2022; Accepted 14 March 2022; Issue published 01 August 2022

Abstract

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 Database (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.

Keywords

Dimensionality reduction; dysphonia detection; linear discriminant analysis; logistic regression; speech feature extraction; support vector machine

Cite This Article

A. Basalamah, M. Hasan, S. Bhowmik and S. A. Shahriyar, "A highly accurate dysphonia detection system using linear discriminant analysis," Computer Systems Science and Engineering, vol. 44, no.3, pp. 1921–1938, 2023.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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