Generalized Replay Spoofing Countermeasure Based on Combining Local Subclassification Models
Source abstract
Abstract
Automatic speaker verification (ASV) systems play a prominent role in the security field due to the usability of voice biometrics compared to alternative biometric authentication modalities. Nevertheless, ASV systems are susceptible to malicious voice spoofing attacks. In response to such threats, countermeasures have been devised to prevent breaches and ensure the safety of user data by categorizing utterances as either genuine or spoofed. In this paper, we propose a new voice spoofing countermeasure that seeks to improve the generalization of supervised learning models. This is accomplished by alleviating the problem of intraclass variance. Specifically, the proposed approach addresses the generalization challenge by splitting the classification problem into a set of local subproblems in order to lessen the supervised learning task. The system outperformed existing state-of-the-art approaches with an EER of 0.097% on the ASVspoof challenge corpora related to replaying spoofing attacks.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Speech Recognition and Synthesis · Speech and Audio Processing · Music and Audio Processing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
Department of Computer Science, College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia
References (76)
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