Very Deep Convolutional Neural Networksfor Robust Speech Recognition
Table 6: WER (%) comparison of various systems on Aurora 4
['Model', 'A', 'B', 'C', 'D', 'AVG']
It is observed that compared to the standard shallow CNN baseline, the performance can be greatly improved by increasing the convolutional layer depth (17.0% relative WER reduction), and gets significant gains on all subsets. Furthermore based on the proposed very deep CNN, adaptation using the auxiliary feature assisted joint training, and joint decoding with an LSTM-RNN model can obtain further very large improvements on this noisy data, giving 25.0% and 33.0% relative WER reductions compared to the baseline. One is without adaptation and the other uses adaptation (adapted on both speaker (83 speakers) and environment (6 noisy scenarios) levels). The systems developed here outperform the others both with and without adaptation.