Replay Attack Detection Based on Voice and Non-voice Sections for Speaker Verification

Abstract

Voice can represent a person's identity. Thus, it can be used in automatic speaker verification (ASV) systems for authenticating secure applications. Unfortunately, existing ASV systems are vulnerable to spoofing attacks. A replay attack is a widely used spoofing technique because it is simple but difficult to detect. Hence, many methods are proposed for countermeasures against replay attacks. Most work inseparably considers voice and non-voice sections in the detection's performance. In this work, we investigate the spoof detection performances when the voice, non-voice, and both with different percentages of voice are used to obtain the optimal section. We also propose a method for detecting replay attacks using the optimal section of a signal. Mel-frequency cepstral coefficients are calculated from the optimal section as a feature, and the ResNet-34 model is used for classification. We evaluated the proposed method using a dataset from the ASVspoof 2019 challenge. The results depict that the optimal section for replay attack detection is when 10% and 20% of voice are included in the non-voice sections. It also showed that the proposed method outperforms the baselines with a 7.52% relatively improvement or an equal error rate of 1.72%.