Improved TDNNs using Deep Kernels and Frequency Dependent Grid-RNNs
Table 2: %WERs for 275h systems on dev17b. Results are with a trigram LM and Viterbi decoding (vit) or CN decoding (cn).
['ID', 'System', 'vit', 'cn']
Each of the models has a hidden layer width of 1000, except the σ-RNN in the Grid-RNNs, which is size 500. The deep kernels continue to give considerable improvement on the larger dataset. Further, the improvement due to the deep kernels scales better to the larger dataset than simply adding hidden layers: ResNet- TDNN+Deep has a 4% relative lower WER than TDNN+Deep in comparison to 2% on the smaller dataset (RTα2 vs. STα4).