  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.
