Exposing speech tampering via spectral phase analysis

Audio recordings serve as important evidence in law enforcement context. The most crucial problem in practical scenarios is to determine whether the audio recording is an authentic one or not. For this task, blind audio tampering detection is typically performed based on electric network frequency (ENF) artifacts. In case there is a high level of noise, ENF analysis would become invalid. In this paper, we present a novel approach to detect and locate tampering in uncompressed audio tracks by analyzing the spectral phase across the Short Time Fourier Transform (STFT) sub-bands. Spectral phase reconstruction is employed to counteract the impact of noise. Also, a new feature based on higher order statistics of the spectral phase residual and the spectral baseband phase correlation between two adjacent voiced segments is proposed to allow for an automated authentication. Experimental results show that a significant increase in detection accuracy can be achieved compared to the conventional ENF-based method when the audio recording is exposed to a high level of noise. We also testify that the proposed method remains robust under various noisy conditions.