Very Deep Convolutional Neural Networksfor Robust Speech Recognition
Table 2: WER (%) comparisons of the models with the context window and frequency dimension extension. F indicates the size on Frequency axis and T indicates the size on Time axis. L indicates the number of convolutional layers
['Model', 'T × F', 'L', 'A', 'B', 'C', 'D', 'AVG']
The typical size of input feature maps in speech recognition is 11×40, which is much smaller than that used in images. Accordingly, the context window and frequency dimension extension in the input feature maps are firstly explored. Following vd6 with 5 convolutions in time and 6 convolutions in frequency, t-ext and f-ext are developed with extended input feature maps. In model t-ext, the context window size is extended to 17 frames allowing 8 convolutional layers stacked in time with a filter size of 3, and f-ext enlarges the FBANK to 64 dimensions which allowing 10 convolutional layers to be stacked. Increasing the number of convolutional layers can obtain a large reduction in WER, and both t-ext and f-ext, in which the extended input feature maps can contribute more convolutional layers, get further significant improvement over the model vd6. Finally the input extension is performed in both time and frequency with 17×64, and the model is named vd10 to indicate that it has 10 convolutional layers. vd10 in Table Moreover most of the contributions are from the three noisy subsets B, C and D, which demonstrates the effect of the proposed VDCNNs for noise robust speech recognition.