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Table 18 Experimental results on the real reverberant data by using NN training data ‘5s.20u’ (combined)

From: Single-channel dereverberation by feature mapping using cascade neural networks for robust distant speaker identification and speech recognition

Method

Frame selection

Acoustic model

Digit accuracy (%)

InCar

Lounge

MeetR

Office

Average

LMCN + Proposed (24 NNs)

Linear 8-1-0

Clean

69.4

38.5

78.9

75.3

65.6

Clean-rt

85.9

60.9

90.3

93.7

82.7

Multi-rt

82.4

85.1

90.9

86.0

86.1

Clean+multi-rt

89.6

91.0

94.4

93.5

92.1

Linear 16-1-0

Clean

67.8

35.1

79.4

78.0

65.1

Clean-rt

85.7

58.8

90.7

94.7

82.5

Multi-rt

82.6

84.2

90.2

89.3

86.6

Clean+multi-rt

89.2

90.9

94.4

93.5

92.0

Linear 32-1-0

Clean

67.0

30.0

74.6

77.6

62.3

Clean-rt

84.7

53.0

89.2

94.1

80.3

Multi-rt

78.4

82.0

88.4

87.9

84.2

Clean+multi-rt

86.5

89.2

93.1

92.0

90.2

Skip1 8-1-0

Clean

69.1

38.0

78.4

75.6

65.3

Clean-rt

85.3

61.3

90.9

93.5

82.8

Multi-rt

81.0

82.5

91.2

84.2

84.7

Clean+multi-rt

89.2

90.7

95.1

93.4

92.1

Skip1 16-1-0

Clean

67.2

34.7

75.8

77.6

63.8

Clean-rt

85.2

58.5

89.6

93.6

81.7

Multi-rt

83.2

84.8

91.5

87.1

86.6

Clean+multi-rt

90.0

92.9

95.5

94.3

93.2

  1. The bold text represents the best average performance.