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.

翻译：验证刑事调查中语音录音证据的完整性是音频取证分析师工作的重要组成部分。其中，一个关键焦点在于检测删除或插入操作，即所谓的音频拼接。尽管这是一种相当简单的篡改口语陈述的方法，但经过精心编辑后，可能产生相当令人信服的结果。对于复杂情况或大量数据，自动化工具可以协助检测潜在的编辑位置。为此，目前已提出了多种分析方法和深度学习方法。然而，很少有方法能够解决实践中预期的非约束性拼接场景。我们提出SigPointer，一个针对连续输入的指针网络框架，能够比现有工作更自然、更高效地揭示拼接位置。在具有取证挑战性的数据（如强压缩和噪声信号）上进行的大量实验，量化了指针机制的优势，性能提升约6到10个百分点。