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The recent integration of generative neural strategies and audio processingtechniques have fostered the widespread of synthetic speech synthesis ortransformation algorithms. This capability proves to be harmful in many legaland informative processes (news, biometric authentication, audio evidence incourts, etc.). Thus, the development of efficient detection algorithms is bothcrucial and challenging due to the heterogeneity of forgery techniques.  This work investigates the discriminative role of silenced parts in syntheticspeech detection and shows how first digit statistics extracted from MFCCcoefficients can efficiently enable a robust detection. The proposed procedureis computationally-lightweight and effective on many different algorithms sinceit does not rely on large neural detection architecture and obtains an accuracyabove 90\% in most of the classes of the ASVSpoof dataset.

The Sound of Silence: Efficiency of First Digit Features in Synthetic Audio Detection