Replay Attack Detection with Complementary High-Resolution Information Using End-to-End DNN for the ASVspoof 2019 Challenge

Jee-weon Jung, Hye-jin Shim, Hee-Soo Heo, Ha-Jin Yu


In this study, we concentrate on replacing the process of extracting hand-crafted acoustic feature with end-to-end DNN using complementary high-resolution spectrograms. As a result of advance in audio devices, typical characteristics of a replayed speech based on conventional knowledge alter or diminish in unknown replay configurations. Thus, it has become increasingly difficult to detect spoofed speech with a conventional knowledge-based approach. To detect unrevealed characteristics that reside in a replayed speech, we directly input spectrograms into an end-to-end DNN without knowledge-based intervention. Explorations dealt in this study that differentiates from existing spectrogram-based systems are twofold: complementary information and high-resolution. Spectrograms with different information are explored, and it is shown that additional information such as the phase information can be complementary. High-resolution spectrograms are employed with the assumption that the difference between a bona-fide and a replayed speech exists in the details. Additionally, to verify whether other features are complementary to spectrograms, we also examine raw waveform and an i-vector based system. Experiments conducted on the ASVspoof 2019 physical access challenge show promising results, where t-DCF and equal error rates are 0.0570 and 2.45% for the evaluation set, respectively.


 DOI: 10.21437/Interspeech.2019-1991

Cite as: Jung, J., Shim, H., Heo, H., Yu, H. (2019) Replay Attack Detection with Complementary High-Resolution Information Using End-to-End DNN for the ASVspoof 2019 Challenge. Proc. Interspeech 2019, 1083-1087, DOI: 10.21437/Interspeech.2019-1991.


@inproceedings{Jung2019,
  author={Jee-weon Jung and Hye-jin Shim and Hee-Soo Heo and Ha-Jin Yu},
  title={{Replay Attack Detection with Complementary High-Resolution Information Using End-to-End DNN for the ASVspoof 2019 Challenge}},
  year=2019,
  booktitle={Proc. Interspeech 2019},
  pages={1083--1087},
  doi={10.21437/Interspeech.2019-1991},
  url={http://dx.doi.org/10.21437/Interspeech.2019-1991}
}