Nextformer: A ConvNeXt Augmented Conformer For End-To-End Speech Recognition

Understand

Conformer models have achieved state-of-the-art(SOTA) results in end-to-end speech recognition.

• However Conformer mainly focuses on temporal modeling while pays less attention on time-frequency property of speech feature.
• In this paper we augment Conformer with ConvNeXt and propose Nextformer structure.
• We use stacks of ConvNeXt block to replace the commonly used subsampling module in Conformer for utilizing the information contained in time-frequency speech feature.