This repository includes implementations of speaker verification systems that input raw waveforms.

Currently, it has three systems in python. Detailed instructions on each system is described in individual ReadME files.

• Code refactoring
  • PyTorch ResNet alike model implementation
  • Deeper architecture
  • Improved feature map scaling method
    • α-feature map scaling for raw waveform speaker verification
      • Only abstract is in English
  • Angular loss function adopted
• Performance
  • EER 1.91%
    • Trained using VoxCeleb2
    • VoxCeleb1 original trial
  • Will be used as a baseline system for authors' future works

• Improved performance than RawNet
  • DNN speaker embedding extraction with raw waveform inputs
  • cosine similarity back-end
  • EER 4.8% -->> 2.56%
    • VoxCeleb1 original trial
• Uses a technique named feature map scaling
  • scales feature map alike squeeze-excitation
• Implemented in PyTorch.
• Published as a conference paper in Interspeech 2020.
  • Improved RawNet with Feature Map Scaling for Text-independent Speaker Verification using Raw Waveforms

@article{jung2020improved,
  title={Improved RawNet with Feature Map Scaling for Text-independent Speaker Verification using Raw Waveforms},
  author={Jung, Jee-weon and Kim, Seung-bin and Shim, Hye-jin and Kim, Ju-ho and Yu, Ha-Jin},
  journal={Proc. Interspeech 2020},
  pages={3583--3587},
  year={2020}
}

• DNN-based speaker embedding extractor used with another DNN-based classifier
  • Built on top of authors' previous works on raw waveform speaker verification
    • ICASSP2018 and Interspeech2018
  • EER 4.8% with cosine simaility back-end, 4.0% with proposed concat&mul back-end
    • VoxCeleb1 original trial
• Implemented in Keras and PyTorch
• Published as a conference paper in Interspeech 2019.
  • RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification

@article{jung2019RawNet,
  title={RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification},
  author={Jung, Jee-weon and Heo, Hee-soo and Kim, ju-ho and Shim, Hye-jin and Yu, Ha-jin},
  journal={Proc. Interspeech 2019},
  pages={1268--1272},
  year={2019}
}