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sphereface2_speaker_verification

official implementation of sphereface2 for speaker verification in Exploring Binary Classification Loss for Speaker Verification, this code is based on wespeaker

advantages:

  1. better performance, especially on hard trials
  2. robust to noisy labels
  3. natural parallelization for the classifier layer

Running

SphereFace2 has been supported in Wespeaker toolkit, see #173. Welcome to use wespeaker for developing and research.

Results

ResNet34-TSTP-emb256

Model Margin Params LM AS-Norm vox1-O-clean vox1-E-clean vox1-H-clean
AAM 0.2 6.63M × × 0.867 1.049 1.959
0.2 × √ 0.787 0.964 1.726
0.5 √ × 0.797 0.937 1.695
0.5 √ √ 0.723 0.867 1.532
C-Sphereface2 0.2 6.63M × × 0.904 0.973 1.737
0.2 × √ 0.835 0.931 1.652
0.3 √ × 0.830 0.862 1.510
0.3 √ √ 0.755 0.833 1.449
A-Sphereface2 0.15 6.63M × × 0.835 0.975 1.742
0.15 × √ 0.761 0.938 1.630
0.25 √ × 0.766 0.899 1.590
0.25 √ √ 0.686 0.852 1.480

Citations

If you find its useful, please cite it as

@inproceedings{han2023exploring,
  title={Exploring Binary Classification Loss for Speaker Verification},
  author={Han, Bing and Chen, Zhengyang and Qian, Yanmin},
  booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={1--5},
  year={2023},
  organization={IEEE}
}

@InProceedings{wen2021sphereface2,
  title = {SphereFace2: Binary Classification is All You Need for Deep Face Recognition},
  author = {Wen, Yandong and Liu, Weiyang and Weller, Adrian and Raj, Bhiksha and Singh, Rita},
  booktitle = {ICLR},
  year = {2022}
}