Deep Recurrent Neural Networks for Acoustic Modelling
Table 3: Ablation effects of our TC-DNN-BLSTM-DNN model. The DNNs and Time Convolution are used for signal and context projections. We show that all components are critical to obtain the best performing model.
['[BOLD] Model', 'dev93 WER', 'eval92 WER']
Finally, our TC-DNN-BLSTM-DNN model combines the DNN-BLSTM-DNN with input time convolution. Our model further improves from 3.76 WER without time convolution to 3.47 WER with time convolution. Compared to the DNN models, we achieve 0.32 absolute WER reduction or 8% relatively. We hypothesize the time convolution gives a richer signal representation to the DNN signal processor and consequently the BLSTM model to consume. The time convolution also relieves the LSTM computation power to learning long term dependencies, rather than short term dependencies.