Very Deep Convolutional Networks for End-to-End Speech Recognition
Table 2: We build deeper encoder networks by adding convolution and residual network blocks. The NiN block equals (L + C (1x1) + B + R) × 2 + L).
['Model', '[BOLD] WER']
We found no gains by simply stacking additional ResLSTM blocks even up to 8 layers. However, we do find gains if we use convolutions. If we stack 2 additional layers of 3 × 3 convolutions our model improves to 11.80% WER or 20% relative over the baseline. If we take this model and add 8 residual blocks (for a total of (2+(8)2+5)=23 layers in the encoder) our model further improves to 11.11% WER, or a 24.7% relative improvement over the baseline. We found that using 8 residual blocks a slightly outperform 4 residual blocks.