Very Deep Convolutional Neural Networksfor Robust Speech Recognition
Table 3: WER (%) comparison of the proposed very deep CNNs with different architectures. F indicates the Frequency axis and T indicates the Time axis. # indicates the number of input feature maps
['Model', '#', 'Pooling', 'Padding', 'A', 'B', 'C', 'D', 'AVG']
All of these previous VDCNNs use pooling in frequency and do not use pooling in time. To investigate whether pooling in time for very deep CNNs with a wider input and a deeper structure is useful, more experiments are conducted. The vd10-tpool is a model with 2 temporal pooling stages. It shows that vd10-fpad is significantly better than vd10 by padding in frequency, and vd10-fpad-tpad padding in both time and frequency obtains a further improvement. These results give the conclusion that padding in feature maps for the very deep CNNs is very important to better encode the border information, which results in a lower WER. Compared to the traditional CNN, we can see that vd10-fpad-tpad containing up to 10 convolutinal layers with appropriate padding and pooling strategies achieves a very large reduction in WER for noise robust speech recognition. All the proposed very deep CNNs are using one feature map as input, i.e. the static FBANK features.