DEFORMER: Coupling Deformed Localized Patterns with Global Context for Robust End-to-end Speech Recognition
This is an incremental improvement for end-to-end speech recognition systems, enhancing robustness by better coupling local and global features.
The paper tackles the problem of rigid convolutional kernels in speech recognition by introducing deformable kernels that adapt to asymmetric patterns, resulting in a 5.6-6.4% relative improvement in word error rate over the Conformer baseline on the WSJ dataset.
Convolutional neural networks (CNN) have improved speech recognition performance greatly by exploiting localized time-frequency patterns. But these patterns are assumed to appear in symmetric and rigid kernels by the conventional CNN operation. It motivates the question: What about asymmetric kernels? In this study, we illustrate adaptive views can discover local features which couple better with attention than fixed views of the input. We replace depthwise CNNs in the Conformer architecture with a deformable counterpart, dubbed this "Deformer". By analyzing our best-performing model, we visualize both local receptive fields and global attention maps learned by the Deformer and show increased feature associations on the utterance level. The statistical analysis of learned kernel offsets provides an insight into the change of information in features with the network depth. Finally, replacing only half of the layers in the encoder, the Deformer improves +5.6% relative WER without a LM and +6.4% relative WER with a LM over the Conformer baseline on the WSJ eval92 set.