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Table 15 Distant-talking speaker identification rates for evaluation data (%)

From: Deep neural network-based bottleneck feature and denoising autoencoder-based dereverberation for distant-talking speaker identification

Method

RT60 of test data (s) (RWCP data)

Ave.

 

0.38

0.47

0.60

0.78

1.30

 

(a) Conventional methods

CMN

79.70

76.05

75.55

74.40

75.75

76.29

MCLMS-SS

82.25

79.70

78.75

78.05

81.30

80.01

MSLP-SS

82.85

78.60

78.50

78.00

75.70

78.73

BF-MLP

72.35

69.30

64.05

64.90

63.25

66.70

(b) DNN-based feature transformation methods

BF-DNN

87.90

84.95

82.45

84.00

82.15

84.29

DAE

92.10

89.70

87.60

89.45

88.10

89.39

DAE + BF-DNN

94.20

92.20

90.65

91.95

90.70

91.94