  Automatic speaker verification (ASV) systems, which determine whether two
speeches are from the same speaker, mainly focus on verification accuracy while
ignoring inference speed. However, in real applications, both inference speed
and verification accuracy are essential. This study proposes cross-sequential
re-parameterization (CS-Rep), a novel topology re-parameterization strategy for
multi-type networks, to increase the inference speed and verification accuracy
of models. CS-Rep solves the problem that existing re-parameterization methods
are unsuitable for typical ASV backbones. When a model applies CS-Rep, the
training-period network utilizes a multi-branch topology to capture speaker
information, whereas the inference-period model converts to a time-delay neural
network (TDNN)-like plain backbone with stacked TDNN layers to achieve the fast
inference speed. Based on CS-Rep, an improved TDNN with friendly test and
deployment called Rep-TDNN is proposed. Compared with the state-of-the-art
model ECAPA-TDNN, which is highly recognized in the industry, Rep-TDNN
increases the actual inference speed by about 50% and reduces the EER by 10%.
The code will be released.
