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The local and global features are both essential for automatic speechrecognition (ASR). Many recent methods have verified that simply combininglocal and global features can further promote ASR performance. However, thesemethods pay less attention to the interaction of local and global features, andtheir series architectures are rigid to reflect local and global relationships.To address these issues, this paper proposes InterFormer for interactive localand global features fusion to learn a better representation for ASR.Specifically, we combine the convolution block with the transformer block in aparallel design. Besides, we propose a bidirectional feature interaction module(BFIM) and a selective fusion module (SFM) to implement the interaction andfusion of local and global features, respectively. Extensive experiments onpublic ASR datasets demonstrate the effectiveness of our proposed InterFormerand its superior performance over the other Transformer and Conformer models.

InterFormer: Interactive Local and Global Features Fusion for Automatic Speech Recognition