Honk: A PyTorch Reimplementation of ConvolutionalNeural Networks for Keyword Spotting
Table 1. Parameters used in the cnn-trad-pool2 model.
['type', '[ITALIC] m', '[ITALIC] r', '[ITALIC] n', '[ITALIC] p', '[ITALIC] q', '[ITALIC] s', '[ITALIC] v']
Full Model Our full model architecture is a faithful reimplementation of the full TensorFlow model, which diverges slightly from the cnn-trad-fpool3 model in the Sainath and Parada paper. The TensorFlow model makes a few changes, selecting p=2 and q=2 and dropping the hidden and linear layers in the original paper. Surprisingly, we confirmed that this leads to better accuracy for our task. We refer to this variant as cnn-trad-pool2. results in 2.77×107+ 7.08×107+3.32×105=9.88×107 multiply operations.