Acoustic features analysis for explainable machine learning-based audio spoofing detection

The rapid evolution of synthetic voice generation and audio manipulation technologies poses significant challenges, raising societal and security concerns due to the risks of impersonation and the proliferation of audio deepfakes. This study introduces a lightweight machine learning (ML)-based framework designed to effectively distinguish between genuine and spoofed audio recordings. Departing from conventional deep learning (DL) approaches, which mainly rely on image-based spectrogram features or learning-based audio features, the proposed method utilizes a diverse set of hand-crafted audio features – such as spectral, temporal, chroma, and frequency-domain features – to enhance the accuracy of deepfake audio content detection. Through extensive evaluation and experiments on three well-known datasets, ASVSpoof2019, FakeAVCelebV2, and an In-The-Wild database, the proposed solution demonstrates robust performance and a high degree of generalization compared to state-of-the-art methods. In particular, our method achieved 89% accuracy on ASVSpoof2019, 94.5% on FakeAVCelebV2, and 94.67% on the In-The-Wild database. Additionally, the experiments performed on explainability techniques clarify the decision-making processes within ML models, enhancing transparency and identifying crucial features essential for audio deepfake detection. • Enhanced spoof audio detection via multi-feature integration. • Employed a lightweight ML framework for real-time applications. • Adopted subject-independent protocols to mitigate biometric bias. • Utilized Explainable AI (XAI) for transparent decision-making.