Exposing Speech Transsplicing Forgery with Noise Level Inconsistency

Splicing is one of the most common tampering techniques for speech forgery in many forensic scenarios. Some successful approaches have been presented for detecting speech splicing when the splicing segments have different signal-to-noise ratios (SNRs). However, when the SNRs between the spliced segm...

Full description

Saved in:
Bibliographic Details
Published in:Security and communication networks 2021-01, Vol.2021, p.1-6
Main Authors: Yan, Diqun, Dong, Mingyu, Gao, Jinxing
Format: Article
Language:English
Subjects:
Algorithms
Datasets
Feature extraction
Forensic sciences
Forgery
Noise
Noise levels
Parameter estimation
Segments
Speech
Speech recognition
Splicing
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Splicing is one of the most common tampering techniques for speech forgery in many forensic scenarios. Some successful approaches have been presented for detecting speech splicing when the splicing segments have different signal-to-noise ratios (SNRs). However, when the SNRs between the spliced segments are close or even same, no effective detection methods have been reported yet. In this study, noise inconsistency between the original speech and the inserted segment from other speech is utilized to detect the splicing trace. First, noise signal of the suspected speech is extracted by a parameter-optimized noise estimation algorithm. Second, the statistical Mel frequency features are extracted from the estimated noise signal. Finally, the spliced region is located by utilizing a change point detection algorithm on the estimated noise signal. The effectiveness of the proposed method is evaluated on a well-designed speech splicing dataset. The comparative experimental results show that the proposed algorithm can achieve better detection performance than other algorithms.
ISSN:1939-0114
1939-0122
DOI:10.1155/2021/6659371