Very Deep Convolutional Neural Networksfor Robust Speech Recognition
Table 5: WER (%) comparisons of VDCNN/RNN joint decoding. ⊕ indicates the normal MBR lattice combination using Kaldi, and ⊗ indicates the state-level weighted log likelihood score combination in joint decoding.
['Model', 'Aux', 'A', 'B', 'C', 'D', 'AVG']
Considering the very deep CNN is one type of feed-forward neural networks, it is interesting to investigate its complementarity with an LSTM-RNN. Hence, an LSTM-RNN was also built and it uses a single frame of 40-dimensional FBANK features with 5 frames shift as input, and the model has three LSTMP (LSTM-Projected) Truncated back propagation through time (BPTT) is used to train the LSTM model with the chunk size set to 20 frames, and 40 utterances are processed in parallel to form a mini-batch. To ensure the stability of training, the gradient is clipped to the range of [−1,1] during parameter update. ⊗ denotes joint decoding. Considering that the performance of VDCNN is better, a slightly biased weight pair, 0.6 and 0.4 for VDCNN and RNN respectively, is used here. For comparison, the normal Kaldi minimum Bayes risk (MBR) lattice combination is also applied with these two systems and denoted with symbol ⊕.