Overlapped Frequency-Distributed Network: Frequency-Aware Voice Spoofing Countermeasure

Sunmook Choi, Il Youp Kwak, Seungsang Oh

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)


Numerous IT companies around the world are developing and deploying artificial voice assistants via their products, but they are still vulnerable to spoofing attacks. Since 2015, the competition “Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof)” has been held every two years to encourage people to design systems that can detect spoofing attacks. In this paper, we focused on developing spoofing countermeasure systems mainly based on Convolutional Neural Networks (CNNs). However, CNNs have translation invariant property, which may cause loss of frequency information when a spectrogram is used as input. Hence, we propose models which split inputs along the frequency axis: 1) Overlapped Frequency-Distributed (OFD) model and 2) Non-overlapped Frequency-Distributed (Non-OFD) model. Using ASVspoof 2019 dataset, we measured their performances with two different activations; ReLU and Max feature map (MFM). The best performing model on LA dataset is the Non-OFD model with ReLU which achieved an equal error rate (EER) of 1.35%, and the best performing model on PA dataset is the OFD model with MFM which achieved an EER of 0.35%.

Original languageEnglish
Pages (from-to)3558-3562
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication statusPublished - 2022
Event23rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2022 - Incheon, Korea, Republic of
Duration: 2022 Sept 182022 Sept 22


  • Deep learning
  • audio deep synthesis
  • countermeasure
  • fake audio detection
  • spoofing

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modelling and Simulation


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