Point to the Hidden: Exposing Speech Audio Splicing via Signal Pointer Nets

TL;DR

This paper introduces SigPointer, a pointer network-based method for detecting audio splicing in voice recordings, demonstrating improved accuracy especially in challenging forensic scenarios with compressed and noisy signals.

Contribution

The paper presents a novel pointer network framework tailored for continuous audio input, enhancing detection of splice locations over existing methods in practical forensic conditions.

Findings

Performance improvements of 6-10 percentage points over previous methods.

Effective detection in strongly compressed and noisy audio signals.

Demonstrated robustness in challenging forensic scenarios.

Abstract

Verifying the integrity of voice recording evidence for criminal investigations is an integral part of an audio forensic analyst's work. Here, one focus is on detecting deletion or insertion operations, so called audio splicing. While this is a rather easy approach to alter spoken statements, careful editing can yield quite convincing results. For difficult cases or big amounts of data, automated tools can support in detecting potential editing locations. To this end, several analytical and deep learning methods have been proposed by now. Still, few address unconstrained splicing scenarios as expected in practice. With SigPointer, we propose a pointer network framework for continuous input that uncovers splice locations naturally and more efficiently than existing works. Extensive experiments on forensically challenging data like strongly compressed and noisy signals quantify the benefit of the pointer mechanism with performance increases between about 6 to 10 percentage points.