Robust speech recognition by selecting mel-filter banks
Yunpeng Wu, Jia-Min Mao, Weifeng Li · 2017
Mel-filterbank energies is a key feature that is widely employed in automatic speech recognition(ASR) system.It arises from a sub-band spectrum typically.But when the noise exists in the background, Mel-filterbank energies can not be easy to estimated accurately.In this paper, the fact that the trajectories of not only "traditional" log Mel-filterbank energies, but also its delta parameters can be influenced by noise will be theoretically analyzed.As a result, log Mel-filterbank energies and their delta parameters can not be calculated correctly.In this paper, we propose to remove those severely contaminated Mel-filterbank features and only keep those variations which perform better in the speech remained.We demonstrate the effectiveness of this novel operation through speech recognition experiments conducted on the Aurora-2 database.