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In this paper, we propose an end-to-end speech recognition network based onNvidia's previous QuartzNet model. We try to promote the model performance, anddesign three components: (1) Multi-Resolution Convolution Module, replaces theoriginal 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 eachconvolutional stream by spatial channel-wise pooling. (3) Multi-Layer FeatureFusion Module, reweights each convolutional block by global multi-layer featuremaps. Our experiments demonstrate that Multi-QuartzNet model achieves CER 6.77%on AISHELL-1 data set, which outperforms original QuartzNet and is close tostate-of-art result.

Multi-QuartzNet: Multi-Resolution Convolution for Speech Recognition with Multi-Layer Feature Fusion