An Investigation of Deep-Learning Frameworks for Speaker Verification Antispoofing

In this study, we explore the use of deep-learning approaches for spoofing detection in speaker verification. Most spoofing detection systems that have achieved recent success employ hand-craft features with specific spoofing prior knowledge, which may limit the feasibility to unseen spoofing attacks. We aim to investigate the genuine-spoofing discriminative ability from the back-end stage, utilizing recent advancements in deep-learning research. In this paper, alternative network architectures are exploited to target spoofed speech. Based on this analysis, a novel spoofing detection system, which simultaneously employs convolutional neural networks (CNNs) and recurrent neural networks (RNNs) is proposed. In this framework, CNN is treated as a convolutional feature extractor applied on the speech input. On top of the CNN processed output, recurrent networks are employed to capture long-term dependencies across the time domain. Novel features including Teager energy operator critical band autocorrelation envelope, perceptual minimum variance distortionless response, and a more general spectrogram are also investigated as inputs to our proposed deep-learning frameworks. Experiments using the ASVspoof 2015 Corpus show that the integrated CNN-RNN framework achieves state-of-the-art single-system performance. The addition of score-level fusion further improves system robustness. A detailed analysis shows that our proposed approach can potentially compensate for the issue due to short duration test utterances, which is also an issue in the evaluation corpus.