Voice Disorder Identification and Classification
Abstract
identification is crucial to prevent long-term damage. This project aims to distinguish pathological from healthy voice samples using spectral
features extracted via Linear Prediction Coefficients (LPC). We apply K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) classifiers, and evaluate their performance using metrics such as precision, true positive rate, and false negative rate. Results show KNN outperforms SVM, achieving 78% accuracy and better robustness in mixed voice conditions.
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DOI: http://dx.doi.org/10.70711/aitr.v2i10.7135
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