A hybrid noise robust model for multireplay attack detection in Automatic speaker verification systems.

Biometric Systems are automatic methods of verifying the identity of a person based on some characteristics such as fingerprint, face, speech etc. Speech biometrics involves verifying the identity of a person based on voice. The process is called speaker verification, and such biometric systems are called Automatic Speaker Verification systems. However, these systems are prone to malicious attacks such as replay attacks. Thus, Automatic Speaker Verification systems need to be robust against these attacks. Existing approaches have worked majorly with single-replay attacks. The proposed work in this paper explores three back-end models: Convolutional Neural Network, Long Short-Term Memory and hybrid of these two models, with different input feature formats. It analyses their performance in a diverse multi-replay attack detection scenario, using the Voice Spoofing Detection Corpus. In the frontend of the proposed hybrid system, mel -spectrograms is used as the feature extraction technique for Convolutional Neural Network based model, and Constant-Q Cepstral Coefficient approach is used for extracting features for Long Short-Term Memory based model. A comparison of this hybrid model's performance is done with past approaches in single-replay attack detection as well. The average Equal Error Rate achieved on the test set was 0.036 for single-replayed attack and 0.0296 for two times replayed. The hybrid model consistently outperforms other models with lower Equal Error Rate values, thus showing promising results, paving the way for future research along this line. Further, an analysis on unseen noisy audio files suggests the proposed model's utility in noise-robust systems as well. [ABSTRACT FROM AUTHOR]