A hybrid approach for binary and multi-class classification of voice disorders using a pre-trained model and ensemble classifiers

Table 5 Performance metrics for the binary classification task of hyperfunctional dysphonia and vocal fold paresis for male and female speakers combined

Gender	Model	Accuracy	F1 Score	PR 0	RE 0	F1 0	PR 1	RE 1	F1 1
Male & Female	VGGish-SVM	68.80 ± 6.79	67.64	0.72	0.82	0.77	0.60	0.46	0.52
	VGGish-LR	63.20 ± 2.40	63.03	0.72	0.70	0.70	0.50	0.52	0.50
	VGGish-MLP	65.37 ± 3.08	65.27	0.73	0.73	0.72	0.53	0.53	0.53
	VGGish-EC	67.10 ± 3.93	66.39	0.72	0.78	0.75	0.57	0.48	0.52

In the metric names, ‘0’ represents hyperfunctional dysphonia class and ‘1’ represents vocal fold paresis. PR, RE and F1 represent Precision, Recall and F1 score respectively. The mean values over folds are presented for all matrices. The highest accuracy is indicated in bold. Additionally, standard deviations for accuracy are provided

ISSN: 1472-6947