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Table 9 Experimental results by using environment models (GMMs) as NNs selector

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

Method Dataset Speaker identification rate (%)
P01 P03 P05 P02 P04 Avg. known Avg. unknown Avg. all
GMM32 + Prop. (24 NNs) + CMN P01/3/5 1s.5u 88.6 90.2 92.8 93.4 90.0 90.5 91.7 91.0
3s.15u 89.5 93.3 94.3 95.3 91.0 92.4 93.2 92.7
GMM32 + Prop. (12 NNs) + CMN P01/3/5 1s.5u 89.4 92.3 92.4 93.5 92.5 91.4 93.0 92.0
3s.15u 91.7 93.5 93.8 96.5 93.5 93.0 95.0 93.8
GMM32 + Prop. (6 NNs) + CMN P01/3/5 1s.5u 90.4 90.9 91.9 94.3 93.7 91.1 94.0 92.2
3s.15u 92.0 92.7 92.0 96.7 94.2 92.2 95.4 93.5
  1. The known environments include P01, P03, and P05, while the unknown environments include P02 and P04. The experiments were done by using the first testing scheme and skip1 7-1-0 frame selection. The bold text represents the best average performance for each training data number.