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

本文提出 InterFormer 方法，使用双向特征交互模块和选择性融合模块实现局部和全局特征的交互和融合，应用于自动语音识别领域，取得比 Transformer 和 Conformer 更好的性能表现。