Autoregressive Model Based Smoothing Forensics Of Very Short Speech Clips

Abstract

Smoothing is a post-processing widely used in speech tampering. Thus, we may determine whether a speech signal is original by smoothing forensics. However, in existing smoothing forensics methods, the detection performance of very short speech clips is much worse than that of long speech clips, and MP3 compression may lead to performance degradation, especially when the length of the smoothing window becomes small. Based on the observation that a very short speech clips can be considered as a stationary autoregressive (AR) process model, we proposed a robust smoothing forensics method of very short speech clips using the AR model coefficients. Experimental results on the TIMIT speech dataset demonstrate that the proposed method significantly outperforms the state-of-the-art method in terms of accuracy and robustness against various MP3 compression.