The result was compared with human performance. ![]() Their performances were evaluated in terms of accuracy, sensitivity, and specificity. The proposed method was tested with six algorithms: support vector machine (SVM), extreme gradient boosting (XGBoost), light gradient boosted machine (LGBM), artificial neural network (ANN), one-dimensional convolutional neural network (1D-CNN) and two-dimensional convolutional neural network (2D-CNN). We extracted features using the software package for speech analysis in phonetics (PRAAT) and calculated the Mel-frequency cepstral coefficients (MFCCs) from voice samples of a vowel sound of /a:/. We investigated whether automated voice signal analysis can be used to distinguish patients with laryngeal cancer from healthy subjects. ![]() ![]() Voice changes may be the earliest signs in laryngeal cancer.
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