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

Table 7 Multi-class classification performance metrics for male and female speakers combined

Gender	Model	Accuracy	F1 Score	PR 0	RE 0	F1 0	PR 1	RE 1	F1 1	PR 2	RE 2	F1 2
Male & Female	VGGish-SVM	70.53 ± 3.22	69.53	0.81	0.83	0.82	0.38	0.36	0.37	0.48	0.39	0.41
	VGGish-LR	61.00 ± 6.29	63.52	0.83	0.67	0.74	0.30	0.43	0.35	0.34	0.52	0.40
	VGGish-MLP	67.85 ± 2.65	67.80	0.80	0.80	0.80	0.38	0.37	0.37	0.36	0.35	0.35
	VGGish-EC	68.34 ± 3.45	68.52	0.81	0.80	0.80	0.38	0.38	0.38	0.40	0.41	0.40

In the metric names, ‘0’ represents the healthy class, ‘1’ represents hyperfunctional dysphonia, and ‘2’ 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