Easy, Interpretable, Effective: openSMILE for voice deepfake detection

Understand

In this paper, we demonstrate that attacks in the latest ASVspoof5 dataset -- a de facto standard in the field of voice authenticity and deepfake detection -- can be identified with surprising accuracy using a small subset of very simplistic features.

• These are derived from the openSMILE library, and are scalar-valued, easy to compute, and human interpretable.
• For example, attack A10`s unvoiced segments have a mean length of 0.09 +- 0.02, while bona fide instances have a mean length of 0.18 +- 0.07.
• Using this feature alone, a threshold classifier achieves an Equal Error Rate (EER) of 10.3% for attack A10.