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

Autor:	Mari, Daniele, Latora, Federica, Milani, Simone
Rok vydání:	2022
Předmět:	Computer Science - Sound Computer Science - Cryptography and Security Computer Science - Machine Learning Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	The recent integration of generative neural strategies and audio processing techniques have fostered the widespread of synthetic speech synthesis or transformation algorithms. This capability proves to be harmful in many legal and informative processes (news, biometric authentication, audio evidence in courts, etc.). Thus, the development of efficient detection algorithms is both crucial and challenging due to the heterogeneity of forgery techniques. This work investigates the discriminative role of silenced parts in synthetic speech detection and shows how first digit statistics extracted from MFCC coefficients can efficiently enable a robust detection. The proposed procedure is computationally-lightweight and effective on many different algorithms since it does not rely on large neural detection architecture and obtains an accuracy above 90\% in most of the classes of the ASVSpoof dataset. Comment: Accepted at WIFS 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2210.02746 Zobrazit plný text záznamu View this record from Arxiv