On noise robustness of dynamic and static features for continuous Cantonese digit recognition

It has been shown previously that augmented spectral features (static and dynamic cepstra) are effective for improving ASR performance in a clean environment. In this paper we investigate the noise robustness of static and dynamic cepstral features, in a speaker independent, continuous recognition task by using a noise-added, Cantonese digit database (CUDigit). We found that the dynamic cepstrum is more robust to additive, background noise than its static counterpart. The results are consistent across different types of noise and under various SNRs. Exponential weights which can exploit the unequal robustness of two features are optimally trained in a development set. A relative word error rate reduction of 41.9%, mainly on a significant reduction of insertions, is obtained on the test data under various noise and SNR conditions.