• Corpus ID: 239024602

Rep Works in Speaker Verification

@article{Ma2021RepWI,
  title={Rep Works in Speaker Verification},
  author={Yufeng Ma and Miao Zhao and Yiwei Ding and Yu Zheng and Min Liu and Minqiang Xu},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.09720}
}
  • Yufeng Ma, Miao Zhao, +3 authors Minqiang Xu
  • Published 19 October 2021
  • Computer Science, Engineering
  • ArXiv
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… 
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References

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TLDR
The proposed serialized multi-layer multi-head attention is designed to aggregate and propagate attentive statistics from one layer to the next in a serialized manner and outperforms other baseline methods by 9.7% in EER and 8.1% in DCF10−2.
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TLDR
The submission of Brno University of Technology (BUT) team to the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2019 is described, a fusion of 4 Convolutional Neural Network (CNN) topologies and the best systems for Fixed and Open conditions achieved 1.42% and 1.26% ERR on the challenge evaluation set respectively.
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TLDR
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