Intra-frame cepstral sub-band weighting and histogram equalization for noise-robust speech recognition

Abstract

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