Deep learning countermeasures for detecting replay speech attacks: a review

Automatic speaker verification (ASV) systems are widely accepted for biometric authentication in real-time applications. However, such ASV systems need robust protection against well-known replay attacks, especially for highly sensitive applications. This study provides a detailed overview of existing deep learning based solutions developed for replay attack detection task. Convolution neural network architectures are most widely explored for countermeasure development. In this study, existing deep learning frameworks are categorized into four groups, namely, spectrogram based deep neural networks (DNN), handcraft features based DNN, source features based DNN and end-to-end DNN frameworks. They have demonstrated notable performances in replay speech detection context. However, their generalisation remains questionable due to the potential challenges posed by unknown types of replay attacks in the future. The study highlights that excitation source features explorations in DNN framework are limited. The study also discussed few existing Gaussian mixture model (GMM) based excitation source explorations, indicating better results may be obtained over existing solutions, using alternative source features based GMM/DNN methods in future. Hence, excitation source information (either implicit or explicit form) may be further explored with GMM/DNN methods. The study concludes with a discussion on potential of excitation source information in replay detection context, following with possible future directions using source information.