| Summary: | 碩士 === 國立暨南國際大學 === 電機工程學系 === 100 === In this study, we develop a novel noise-robustness method, termed weighted sub-band level histogram equalization (WS-HEQ), to promote the speech recognition accuracy in a noise-corrupted environment. Based on the observation that the high-pass and low-pass portions of the intra-frame cepstral features possess unequal importance for speech recognition and different signal-to-noise ratios (SNRs), WS-HEQ intends to alleviate the high-pass portion in order to highlight the speech components and reduce the effect of noise. Furthermore, we provide four variants of WS-HEQ, which primarily refer to the structure of sub-band level histogram equalization (S-HEQ).
In the experiments conducted on the Aurora-2 connected US digit database, we show that all the presented four variants of WS-HEQ give significant recognition improvements relative to the MFCC baseline in various noise-corrupted situations. WS-HEQ outperforms HEQ in recognition accuracy, and it behaves better than S-HEQ in most cases. Besides, WS-HEQ can be implemented more efficiently than S-HEQ since fewer HEQ processes are needed in WS-HEQ than S-HEQ.
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