Anas Basalamah1, Mahedi Hasan2, Shovan Bhowmik2, Shaikh Akib Shahriyar2,*
Computer Systems Science and Engineering, Vol.44, No.3, pp. 1921-1938, 2023, DOI:10.32604/csse.2023.027399
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… More >
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