  In this paper, we propose an end-to-end speech recognition network based on
Nvidia's previous QuartzNet model. We try to promote the model performance, and
design three components: (1) Multi-Resolution Convolution Module, replaces the
original 1D time-channel separable convolution with multi-stream convolutions.
Each stream has a unique dilated stride on convolutional operations. (2)
Channel-Wise Attention Module, calculates the attention weight of each
convolutional stream by spatial channel-wise pooling. (3) Multi-Layer Feature
Fusion Module, reweights each convolutional block by global multi-layer feature
maps. Our experiments demonstrate that Multi-QuartzNet model achieves CER 6.77%
on AISHELL-1 data set, which outperforms original QuartzNet and is close to
state-of-art result.
