Very Deep Convolutional Networks for End-to-End Speech Recognition
Table 1: We build deeper encoder networks by adding NiN modules inbetween LSTM layers.
['Model', '[BOLD] WER']
We can further increase the depth of the network by adding more NiN 1 × 1 convolution modules inbetween each LSTM layer. This improves our model’s performance further to 12.88% WER or 12.7% relative over the baseline. The BN layers were critical, and without them we found the model did not converge well.