2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPE-26.3
Paper Title A CAPSULE NETWORK BASED APPROACH FOR DETECTION OF AUDIO SPOOFING ATTACKS
Authors Anwei Luo, Enlei Li, Sun Yat-sen University, China; Yongliang Liu, Alibaba Group, China; Xiangui Kang, Sun Yat-sen University, China; Z. Jane Wang, University of British Columbia, Canada
SessionSPE-26: Speaker Verification Spoofing and Countermeasures
LocationGather.Town
Session Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Poster
Topic Speech Processing: [SPE-SPKR] Speaker Recognition and Characterization
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Audio spoofing attacks not only increasingly pose a threat to automatic speaker verification systems but also have the potential to destabilize national security (e.g., by creating fake audio of influential politicians). The main purpose of anti-spoofing is to detect fake audios synthesized by advanced methods, while current algorithms using convolutional neural networks as classifiers exposed poor generalization to the unknown attacks. In this paper, as the first attempt, we introduce a capsule network to enhance the generalization of the detection system. To make the capsule network suitable for anti-spoofing tasks, we modified the original dynamic routing algorithm to force the model to pay more attention to artifacts and thus yield better detection performance for text-to-speech/voice conversion attacks. Furthermore, replay attack detection is also investigated, and the results indicate that our proposed approach is also highly capable of detecting replay attacks.