Audio Replay Attack Detection with Deep Learning Frameworks

Galina Lavrentyeva, Sergey Novoselov, Egor Malykh, Alexander Kozlov, Oleg Kudashev, Vadim Shchemelinin

Nowadays spoofing detection is one of the priority research areas in the field of automatic speaker verification. The success of Automatic Speaker Verification Spoofing and Countermeasures (ASVspoof) Challenge 2015 confirmed the impressive perspective in detection of unforeseen spoofing trials based on speech synthesis and voice conversion techniques. However, there is a small number of researches addressed to replay spoofing attacks which are more likely to be used by non-professional impersonators. This paper describes the Speech Technology Center (STC) anti-spoofing system submitted for ASVspoof 2017 which is focused on replay attacks detection. Here we investigate the efficiency of a deep learning approach for solution of the mentioned-above task. Experimental results obtained on the Challenge corpora demonstrate that the selected approach outperforms current state-of-the-art baseline systems in terms of spoofing detection quality. Our primary system produced an EER of 6.73% on the evaluation part of the corpora which is 72% relative improvement over the ASVspoof 2017 baseline system.

 DOI: 10.21437/Interspeech.2017-360

Cite as: Lavrentyeva, G., Novoselov, S., Malykh, E., Kozlov, A., Kudashev, O., Shchemelinin, V. (2017) Audio Replay Attack Detection with Deep Learning Frameworks. Proc. Interspeech 2017, 82-86, DOI: 10.21437/Interspeech.2017-360.

  author={Galina Lavrentyeva and Sergey Novoselov and Egor Malykh and Alexander Kozlov and Oleg Kudashev and Vadim Shchemelinin},
  title={Audio Replay Attack Detection with Deep Learning Frameworks},
  booktitle={Proc. Interspeech 2017},