Densely Connected Convolutional Networks for Speech Recognition
Table 6: Comparison of networks with different hyper-parameters in terms of WER (%) on dev’93 and eval’92 and the training time when systems were trained with 78 hours WSJ data set.
['System', '# of', 'depth', '[ITALIC] θ', 'WER', 'Train']
In the previous experiments on RM data set, we also fixed the depth to 22 or 41 and the dense blocks to 3. The DenseNet-C with depth 41 performed the best with 10 days training time. After increasing the number of dense blocks, the training time got improved a lot without losing much performance. Then we tried DenseNet-C with depth 61 and θ=0.4 which reached the best WER with the RM data set, the result shows that this model still performed well on WSJ data set.