Multi-Band Processing With Gabor Filters and Time Delay Neural Nets for Noise Robust Speech Recognition

Spectro-temporal feature extraction and multi-band processing were both invented with the goal of increasing the robustness of speech recognisers. However, although these methods have been in use for a long time now, and they are evidently compatible, few attempts have been made to combine them. This is why here we investigate the combination of multi-band processing with the use of spectro-temporal Gabor filters. First, based on the TIMIT corpus, we optimise their meta-parameters like the overlap, and the number of bands. Then we verify the cross-corpus viability of our multi-band processing approach on the Aurora-4 corpus. Lastly, we combine our method with the recently proposed channel dropout method. Our results show that this combination not only leads to lower error rates than those got using either multi-band processing or channel dropout, but these results compare favourably to those recently reported for the clean training scenario on the Aurora-4 corpus.