Comparative Analysis Of ML/DL Models for Voice Spoofing Detection
R. Uma Rani, Bal Kishan · International Journal of Basic and Applied Sciences · 2025
Spoofing of voice is an alarming threat to voice-security systems, especially in high-stakes areas like banking, smart home gadgets, customer support, and virtual assistants. In this paper, the authors submit an inclusive comparative review of spoof detection methods with a description of how they evolved from classical machine learning to the latest deep learning and transformer-based models. The performance of different models is measured across benchmark datasets with common metrics like Equal Error Rate (EER) and tandem Detection Cost Function (t-DCF). Interestingly, the increase in the effectiveness of Transformer architectures for spoofed audio detection is emphasized. The ultimate goal of the present research is to assist both professionals and academics in selecting and developing dependable and safe voice authentication systems.