ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context
Table 4: The effect of temporal reduction and convolution kernel size on FLOPS and model accuracy.
['[BOLD] Reduction', '[BOLD] Kernel size', '[BOLD] GFLOPS', '[BOLD] testclean', '[BOLD] testother']
Downsampling and kernel size: We use the same model with only one downsampling layer added to C3 as the baseline; hence the baseline only does 2× temporal reduction. We sweep the kernel size in {3,5,11,21}, each kernel size is applied to all the depthwise convolution layers. The results suggest that progressive downsampling introduces significant saving in the number of FLOPS. Moreover, it actually benefits the accuracy of the model slightly. In addition, with progressive downsampling, increasing the kernel size decreases the WER of the model.