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Table 5 Experimental results on the use of modified multiple NNs configuration for known positions (matched conditions)

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

Method

Dataset

Error rate reduction (%)

P01

P02

P05

Avg.

Proposed (1 NN) + CMN

1s.1u

30.8

19.4

21.7

24.0

1s.5u

34.7

27.8

23.5

28.7

3s.15u

35.4

33.3

23.5

30.8

Proposed (6 NNs) + CMN

1s.1u

40.8

44.0

19.7

34.8

1s.5u

45.5

62.7

16.3

41.5

3s.15u

51.0

71.4

23.5

48.7

Proposed (12 NNs) + CMN

1s.1u

33.0

33.7

22.9

29.8

1s.5u

40.6

54.0

32.0

42.2

3s.15u

55.2

71.4

27.5

51.4

Proposed (24 NNs) + CMN

1s.1u

17.9

2.5

10.5

10.3

1s.5u

30.2

31.0

22.2

27.8

3s.15u

46.9

42.9

13.7

34.5

  1. The experiments were done by using the first testing scheme and skip1 3-1-0 frame selection. The results are shown in term of ERR relative to the ‘CMN’. The bold text represents the best average performance for each training data number.