  Multi-branch convolutional neural network architecture has raised lots of
attention in speaker verification since the aggregation of multiple parallel
branches can significantly improve performance. However, this design is not
efficient enough during the inference time due to the increase of model
parameters and extra operations. In this paper, we present a new multi-branch
network architecture RepSPKNet that uses a re-parameterization technique. With
this technique, our backbone model contains an efficient VGG-like inference
state while its training state is a complicated multi-branch structure. We
first introduce the specific structure of RepVGG into speaker verification and
propose several variants of this structure. The performance is evaluated on
VoxCeleb-based test sets. We demonstrate that both the branch diversity and the
branch capacity play important roles in RepSPKNet designing. Our RepSPKNet
achieves state-of-the-art performance with a 1.5982% EER and a 0.1374 minDCF on
VoxCeleb1-H.
