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核心组成部分。其中，重点在于检测删除或插入操作，即所谓的音频拼接。尽管这是一种相对简单的篡改口头陈述的方法，但经过精心编辑后可能产生极具说服力的结果。对于复杂案例或大量数据，自动化工具可辅助检测潜在的编辑位置。为此，目前已有多种分析方法和深度学习方法被提出。然而，很少有方法能解决实际应用中预期的无约束拼接场景。我们提出SigPointer，一种面向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。针对法医学中具有挑战性的数据（如强压缩和噪声信号）进行的大量实验表明，指针机制带来了约6至10个百分点的性能提升。