In this paper, we propose a novel noise-robustness method known as weighted sub-band histogram equalization\r\n(WS-HEQ) to improve speech recognition accuracy in noise-corrupted environments. Considering the observations\r\nthat high- and low-pass portions of the intra-frame cepstral features possess unequal importance for noise-corrupted\r\nspeech recognition, WS-HEQ is intended to reduce the high-pass components of the cepstral features. Furthermore,\r\nwe provide four types of WS-HEQ, which partially refers to the structure of spatial histogram equalization (S-HEQ). In\r\nthe experiments conducted on the Aurora-2 noisy-digit database, the presented WS-HEQ yields significant\r\nrecognition improvements relative to the Mel-scaled filter-bank cepstral coefficient (MFCC) baseline and to cepstral\r\nhistogram normalization (CHN) in various noise-corrupted situations and exhibits a behavior superior to that of S-HEQ\r\nin most cases.
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