A robust voice spoofing detection system using novel CLS-LBP features and LSTM

Abstract

Automatic Speaker Verification (ASV) systems are vulnerable to a variety of voice spoofing attacks, e.g., replays, speech synthesis, etc. The imposters/fraudsters often use different voice spoofing attacks to fool the ASV systems to achieve certain objectives, i.e., bypass the security of someone’s home or stealing money from a bank account, etc. To counter such fraudulent activities on the ASV systems, we propose a robust voice spoofing detection system capable of effectively detecting multiple types of spoofing attacks. For this purpose, we propose a novel feature descriptor Center Lop-Sided Local Binary Patterns (CLS-LBP) for audio representation. CLS-LBP effectively analyzes the audios bidirectionally to better capture the artifacts of synthetic speech, microphone distortions of replay, and dynamic speech attributes of the bonafide signal. The proposed CLS-LBP features are used to train the long short-term memory (LSTM) network for detection of both the physical- (replay) and logical-access attacks (speech synthesis, voice conversion). We employed the LSTM due to its effectiveness to better process and learn the internal representation of sequential data. More specifically, we obtained an equal error rate (EER) value of 0.06% on logical-acess (LA) while 0.58% on physical-access (PA) attacks. Additionally, the proposed system is also capable of detecting the unseen voice spoofing attacks and also robust enough to classify among the cloning algorithms used to synthesize the speech. Performance evaluation on the ASVspoof 2019 corpus signify the effectiveness of the proposed system in terms of detecting the physical- and logical-access attacks over existing state-of-the-art voice spoofing detection systems.