In this paper,an Enhanced Self-Attention (ESA) mechanism has been put forward
for robust feature extraction.The proposed ESA is integrated with the recursive
gated convolution and self-attention mechanism.In particular, the former is
used to capture multi-order feature interaction and the latter is for global
feature extraction.In addition, the location of interest that is suitable for
inserting the ESA is also worth being explored.In this paper, the ESA is
embedded into the encoder layer of the Transformer network for automatic speech
recognition (ASR) tasks, and this newly proposed model is named GNCformer. The
effectiveness of the GNCformer has been validated using two datasets, that are
Aishell-1 and HKUST.Experimental results show that, compared with the
Transformer network,0.8%CER,and 1.2%CER improvement for these two mentioned
datasets, respectively, can be achieved.It is worth mentioning that only 1.4M
additional parameters have been involved in our proposed GNCformer.

本文提出了增强的自注意机制（ESA）用于鲁棒特征提取，并将其融合到 Transformer 网络的编码层中进行自动语音识别（ASR）任务，这一新模型被命名为 GNCformer。通过 Aishell-1 和 HKUST 数据集的实验证明，GNCformer 相较于 Transformer 网络，能分别提高 0.8% 和 1.2％的字符错误率（CER），并且只增加了 1.4M 的额外参数量。