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Table 3 Comparison of average recognition rates and percentage of improvement in comparison to MFCC for various feature types on three test sets of Aurora 2 task

From: Autocorrelation-based noise subtraction method with smoothing, overestimation, energy, and cepstral mean and variance normalization for noisy speech recognition

Feature type Recognition rate (%) Percentage of improvement (%) Overall average Overall average improvement (%)
Set A Set B Set C Set A Set B Set C
MFCC 61.13 55.57 66.68 61.13
AMFCC 63.41 57.67 69.72 5.87 4.73 9.12 63.60 6.57
RAS 66.77 60.94 71.81 14.51 12.09 15.40 66.51 14.00
DAS 70.90 65.57 77.17 25.14 22.51 31.48 71.21 26.37
SPFH 73.61 68.98 80.89 32.11 30.18 42.65 74.49 34.98
MFCC-SS 69.22 63.46 73.60 20.81 17.76 20.77 68.76 19.78
MVA 76.05 76.35 73.10 38.38 46.77 19.27 75.17 34.81
ANS 77.10 74.32 83.61 41.09 42.20 50.81 78.34 44.70
Kernel 78.90 75.88 84.53 45.72 45.71 53.57 79.77 48.33
ANSS 80.47 79.04 85.53 49.76 52.82 56.57 81.68 53.05
ANS + OEP 78.78 75.98 84.14 45.41 45.94 52.40 79.63 47.92
Kernel + OEP 80.05 77.86 85.40 48.68 50.17 56.18 81.10 51.68
ANSS + OEP 82.37 81.10 86.21 54.64 57.46 58.61 83.23 56.91
ANSSOEMV 84.81 86.63 87.97 60.92 69.91 63.90 86.47 64.91
ETSI-XAFE 86.56 85.19 83.49 65.42 66.67 50.45 85.08 60.85