EURASIP Journal on Advances in Signal Processing

Table 1 Performance of the MFCC-based GMM method

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

Method	Confusion matrix		Accuracy (%)	Specify (%)	Sensitivity (%)	AUC (%)
GMM 8 mixtures	89.58	10.42	89.35 ± 3.00	89.67	89.53	95.95
	10.88	89.12
GMM 16 mixtures	89.58	8.33	92.00 ± 4.79	92.27	91.72	98.59
	7.68	92.32
GMM 32 mixtures	89.58	10.42	90.31 ± 4.09	90.90	89.73	96.02
	8.96	91.04

The best performance is highlighted in bold.

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