Classical Machine Learning Shines with 93% Accuracy in Deepfake Audio Detection

This exciting research demonstrates that interpretable, classical machine learning models can effectively combat the rising threat of synthetic speech fraud. By identifying specific acoustic cues like pitch variability and spectral richness, the study provides a transparent and highly accurate alternative to complex neural networks. Achieving a remarkable 93% accuracy across both high-fidelity and telephone-quality audio, these models offer a powerful, understandable baseline for future security systems.