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

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 pro-pose 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%.