  Transformer has achieved extraordinary performance in Natural Language
Processing and Computer Vision tasks thanks to its powerful self-attention
mechanism, and its variant Conformer has become a state-of-the-art architecture
in the field of Automatic Speech Recognition (ASR). However, the main-stream
architecture for Automatic Speaker Verification (ASV) is convolutional Neural
Networks, and there is still much room for research on the Conformer based ASV.
In this paper, firstly, we modify the Conformer architecture from ASR to ASV
with very minor changes. Length-Scaled Attention (LSA) method and
Sharpness-Aware Minimizationis (SAM) are adopted to improve model
generalization. Experiments conducted on VoxCeleb and CN-Celeb show that our
Conformer based ASV achieves competitive performance compared with the popular
ECAPA-TDNN. Secondly, inspired by the transfer learning strategy, ASV Conformer
is natural to be initialized from the pretrained ASR model. Via parameter
transferring, self-attention mechanism could better focus on the relationship
between sequence features, brings about 11% relative improvement in EER on test
set of VoxCeleb and CN-Celeb, which reveals the potential of Conformer to unify
ASV and ASR task. Finally, we provide a runtime in ASV-Subtools to evaluate its
inference speed in production scenario. Our code is released at
https://github.com/Snowdar/asv-subtools/tree/master/doc/papers/conformer.md.
