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In speaker verification, the convolutional neural networks (C-NN) have been successfully leveraged to achieve a great performance. Most of the models based on CNN primarily focus on learning the distinctive speaker embedding from the horizontal direction (time-axis). However, the feature relationship between channels is usually neglected. In this paper, we firstly aim toward an alternate direction of recalibrating the channelwise features by introducing the recently proposeddoi:10.21437/interspeech.2019-1704 dblp:conf/interspeech/ZhouJLLH19 fatcat:6va5knr4cnf4lhh2mjlpecybua