The reliability of voice-based authentication has increased with the adoption of voice-controlled technologies and digital transactions. Automatic Speaker Verification (ASV) provides a dependable approach due to its special capacity to confirm identity based on speech. ASV is mostly used in telecommunications, banking, law enforcement, and smart assistants to increase security and user comfort. However, spoofing attacks like voice conversion and speech synthesis are increasingly targeting these systems, making them less compatible, examining responses to new kinds of attacks through data augmentation, and highlighting the role of transfer learning in improving detection even when there is a lack of data. This review discusses the importance of strengthening ASV systems with data augmentation to address new threats, transfer learning to enhance detection with limited data, and adaptive models to keep up with advancing spoofing attacks.
Rani et al. (Sun,) studied this question.