RawBoost: A Raw Data Boosting and Augmentation Method applied to Automatic Speaker Verification Anti-Spoofing

@article{Tak2021RawBoostAR,
  title={RawBoost: A Raw Data Boosting and Augmentation Method applied to Automatic Speaker Verification Anti-Spoofing},
  author={Hemlata Tak and Madhu R. Kamble and Jose Patino and Massimiliano Todisco and Nicholas W. D. Evans},
  journal={ArXiv},
  year={2021},
  volume={abs/2111.04433}
}
This paper introduces RawBoost, a data boosting and augmentation method for the design of more reliable spoofing detection solutions which operate directly upon raw waveform inputs. While RawBoost requires no additional data sources, e.g. noise recordings or impulse responses and is data, application and model agnostic, it is designed for telephony scenarios. Based upon the combination of linear and non-linear convolutive noise, impulsive signal-dependent additive noise and stationary signal… 
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