EVA-ASCA: Enhancing Voice Anti-Spoofing through Attention-based Similarity Weights and Contrastive Negative Attractors

Nghi Tran, Bima Prihasto, Phuong Thi Le, Thao Tran, Chun Shien Lu, Jia Ching Wang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

Voice spoofing attacks pose an escalating security concern within the contemporary digital landscape. Attackers employ techniques such as voice conversion (VC) and text-to-speech (TTS) to generate a synthetic speech that replicates the victim's voice, to illicitly access sensitive data. Detection of these attacks hinges on identifying anomalies in audio transmission resulting from these deceptive activities. Anomalies arise from encoding and transmission conditions that are not commonly encountered, particularly in situations such as local authentication or telephony. To address this issue, our study presents a strategy featuring pivotal enhancement: Attention-based Similarity Weights and Contrastive Negative Attractors. This technique clusters authentic speeches around multiple speaker attractors together in a high-dimensional embedding space, effectively thwarting spoofing attacks across all attractors. Experimental results substantiate the superiority of our system, yielding a substantial 1.09% improvement in the equal error rate (EER) when compared to existing solutions on the ASVspoof 2019 LA evaluation dataset.

原文???core.languages.en_GB???
主出版物標題Proceedings - 2024 IEEE Conference on Artificial Intelligence, CAI 2024
發行者Institute of Electrical and Electronics Engineers Inc.
頁面537-540
頁數4
ISBN(電子)9798350354096
DOIs
出版狀態已出版 - 2024
事件2nd IEEE Conference on Artificial Intelligence, CAI 2024 - Singapore, Singapore
持續時間: 25 6月 202427 6月 2024

出版系列

名字Proceedings - 2024 IEEE Conference on Artificial Intelligence, CAI 2024

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???event.eventtypes.event.conference???2nd IEEE Conference on Artificial Intelligence, CAI 2024
國家/地區Singapore
城市Singapore
期間25/06/2427/06/24

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