Static and dynamic spectral features: their noise robustness and optimal weights for ASR

TLDR: It is found that the dynamic cepstrum is more robust to additive noise than its static counterpart, and a simple yet effective strategy of exponentially weighting the likelihoods that are contributed by the static and dynamic features during the decoding process is proposed.

Abstract: In this paper, we investigate the relative noise robustness between dynamic and static spectral features, by using two speaker independent continuous digit databases in English (Aurora2) and Cantonese (CUDigit) It is found that the dynamic cepstrum is more robust to additive noise than its static counterpart The results are consistent across different types of noise and under various SNRs Optimal exponential weights for exploiting unequal noise robustness of the two features are discriminatively trained in a development set When tested under various noise conditions, the optimal weights yielded relative word error rate reductions of 366% and 419% for Aurora2 and CUDigit, respectively The proposed weighting is attractive for many ASR applications in noise because: (1) no noise estimation for feature compensation; (2) no adaptation of clean HMMs to a noisy environment; and (3) only a trivial change in the decoding process by weighting log likelihoods of static and dynamic components separately