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

Table 2 Performance metrics for the binary classification task of healthy vs. disordered for male and female speakers

Gender	Model	Accuracy	F1 Score	PR 0	RE 0	F1 0	PR 1	RE 1	F1 1
Male	VGGish-SVM	82.45 ± 2.77	82.99	0.91	0.87	0.89	0.54	0.64	0.58
	VGGish-LR	75.35 ± 4.30	75.45	0.85	0.84	0.84	0.41	0.41	0.40
	VGGish-MLP	77.09 ± 5.75	76.96	0.86	0.86	0.86	0.43	0.42	0.42
	VGGish-EC	80.25 ± 5.70	79.66	0.86	0.89	0.88	0.51	0.44	0.47
	wav2vec-SVM [45]	75.65 ± 5.81	-	0.91	0.82	0.87	0.50	0.69	0.58
	MFCC-glottal-SVM [45]	74.48 ± 5.85	-	0.90	0.84	0.87	0.51	0.64	0.57
	MFCC-SVM [45]	72.02 ± 7.75	-	0.89	0.88	0.88	0.54	0.56	0.55
	HuBERT-SVM [45]	72.14 ± 7.93	-	0.89	0.85	0.87	0.50	0.59	0.54
Female	VGGish-SVM	70.03 ± 3.07	70.05	0.79	0.77	0.77	0.53	0.57	0.54
	VGGish-LR	66.31 ± 4.86	66.68	0.77	0.72	0.74	0.48	0.55	0.51
	VGGish-MLP	68.36 ± 3.76	68.11	0.76	0.78	0.77	0.51	0.49	0.50
	VGGish-EC	71.54 ± 4.13	71.83	0.80	0.76	0.78	0.56	0.62	0.58
	wav2vec-SVM [45]	73.80 ± 5.03	-	0.84	0.77	0.80	0.60	0.71	0.65
	MFCC-glottal-SVM [45]	66.13 ± 3.11	-	0.80	0.66	0.72	0.49	0.66	0.56
	MFCC-SVM [45]	68.15 ± 4.59	-	0.81	0.68	0.74	0.51	0.68	0.58
	HuBERT-SVM [45]	74.50 ± 4.38	-	0.85	0.76	0.81	0.60	0.72	0.65

In the metric names, ‘0’ corresponds to the healthy class, and ‘1’ represents the disordered. 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