Core Concepts
SigPointer, a pointer network framework, can efficiently uncover splice locations in speech audio signals, outperforming existing methods on forensically challenging data.
Abstract
The paper proposes a novel approach called SigPointer for detecting and localizing audio splicing in speech recordings. Audio splicing, which involves deleting, copying, or inserting speech segments, is an effective way to manipulate audio evidence and poses a challenge for forensic analysts.
The key highlights are:
SigPointer treats audio splicing localization as a pointing task, where the neural network directly predicts the positions of splice points in the input signal. This is more natural and efficient than previous approaches that classify fixed-size segments or learn a mapping to a fixed vocabulary.
SigPointer is designed for continuous input signals, unlike existing pointer methods that operate on categorical data. It uses a Transformer-based encoder-decoder architecture with a pointer mechanism in the decoder.
Extensive experiments on forensically challenging data, including strongly compressed and noisy signals, show that SigPointer outperforms several baseline methods, including CNN-based and sequence-to-sequence Transformer models. The performance improvements range from 6 to 10 percentage points in Jaccard index and recall.
SigPointer demonstrates strong robustness to complex post-processing chains, such as multiple compression and real-world noise, outperforming the best existing model by 8-9 percentage points.
The proposed pointer framework allows SigPointer to have a much smaller model size compared to other methods, while still achieving superior performance.
Overall, SigPointer represents a significant advancement in audio splicing localization, providing a more natural and efficient solution for forensic analysts dealing with unconstrained audio data.
Stats
The dataset used for training and evaluation consists of speech audio samples with 0 to 5 splicing positions, subjected to various post-processing operations such as compression and additive noise.
Quotes
"Verifying the integrity of voice recording evidence for criminal investigations is an integral part of an audio forensic analyst's work."
"With powerful tools, either commercial or free, as for example Audacity [1], the hurdles for editing operations have become low."
"Forensic audio analysts are thus often assigned to verify the integrity of material relevant to court cases."