Multi-scale Octave Convolutions for Robust Speech Recognition
Table 2: WERs [%] for models trained on AMI MDM and evaluated on IHM, SDM and MDM conditions. ”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)', 'IHM dev', 'IHM eval', 'SDM dev', 'SDM eval', 'MDM dev', 'MDM eval']
AMI: In contrast to the Aurora-4 findings, better performance was achieved with an all OctCNN model (L1-L15). This is an interesting finding, and we believe that the multi-scale processing of the input feature space is beneficial for AMI MDM because of the reverberation in the data. The reverbarated input time×freq representation can be viewed as a spatially redundant one, therefore the OctConv layer applied to the input representation is effective. Unfortunately, the only MultiOctConv model superior to the baseline CNN is the one with 3 groups with a spatial reduction by 1 and 2 octaves. This result indicates that the spatial redundancy for this architecture for AMI MDM is not degrading the performance. However, in terms of the computational cost, we can reduce the #MACCs by a factor of 1.8 with only a small WER increase for a model with 4 resolution groups.