A two-stage approach using Gaussian mixture models and higher-order statistics for a classification of normal and pathological voices

EURASIP Journal on Advances in Signal Processing

Table 3 Performance of the proposed two-stage classifiers

Method		Confusion matrix		Accuracy (%)	Specificity (%)	Sensitivity (%)	AUC (%)
GMM 8 mixtures	Mean of the normalized skewness ( ${\bar{γ}}_{3}$ )	93.75	6.25	94.00 ± 1.67	94.21	93.78	99.69
		5.76	94.24
	Mean of the normalized kurtosis ( ${\bar{γ}}_{4}$ )	93.75	6.25	94.00 ± 1.67	94.21	93.78	99.69
		5.76	94.24
GMM 16 mixtures	Mean of the normalized skewness( ${\bar{γ}}_{3}$ )	95.83	4.17	96.64 ± 4.09	97.43	95.90	99.69
		2.56	97.44
	Mean of the normalize kurtosis ( ${\bar{γ}}_{4}$ )	95. 83	4. 17	96. 96 ± 4. 79	98. 04	95. 92	99. 95
		1.92	98.08
GMM 32 mixtures	Mean of the normalized skewness ( ${\bar{γ}}_{3}$ )	93.75	6.25	94.64 ± 1.92	95.44	93.86	99.69
		4.48	95.52
	Mean of the normalized kurtosis ( ${\bar{γ}}_{4}$ )	95.83	4.17	96.00 ± 3.13	96.15	95.84	99.87
		3.84	96.16