Multi-scale Octave Convolutions for Robust Speech Recognition
Table 1: WERs [%] for Aurora-4 test sets A, B, C and D for octave and multi-octave CNNs. ”OctConv” column indicates where a Conv layer was replaced with an OctConv or MultiOctConv. 21, 22 and 23 correspond to the factor of spatial dimension reduction. Models with batch normalization after ReLU are denoted by †.
['Model', 'OctConv', '[ITALIC] α (low → high)', '21', '22', '23', '#MACCs (M)', 'A', 'B', 'C', 'D', 'Avg.']
Aurora-4: We replace vanilla convolutional layers of our baseline model (CNN) with OctConv and MultiOctConv layers. We first evaluate which layers can be replaced and find that all but the first layer, operating directly on the input representation, should be replaced for the best performance. This approach (L2-L15) is also the least costly. Reducing the ratio of low-resolution representations to 0.125 improves the WER for the mismatched microphone scenario C, but not for all test conditions. Applying batch normalization after ReLU is beneficial for test set C and D. For OctCNN models, the WER for test set D dropped by ∼0.4% with a compression by one octave, and by another ∼0.4% with a reversed batch normalization and ReLU order.