Synthetic Voice Detection and Audio Splicing Detection using SE-Res2Net-Conformer Architecture
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Abstract (arXiv)
Synthetic voice and splicing audio clips have been generated to spoof Internet users and artificial intelligence (AI) technologies such as voice authentication. Existing research work treats spoofing countermeasures as a binary classification problem: bonafide vs. spoof. This paper extends the existing Res2Net by involving the recent Conformer block to further exploit the local patterns on acoustic features. Experimental results on ASVspoof 2019 database show that the proposed SE-Res2Net-Conformer architecture is able to improve the spoofing countermeasures performance for the logical access scenario. In addition, this paper also proposes to re-formulate the existing audio splicing detection problem. Instead of identifying the complete splicing segments, it is more useful to detect the boundaries of the spliced segments. Moreover, a deep learning approach can be used to solve the problem, which is different from the previous signal processing techniques.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 30 references that are in the map. First posted on arXiv 7 Oct 2022.
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16 of 30 references.
References · 16
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- 2020Replay and Synthetic Speech Detection with Res2net Architecturecited by 39
- 2020DeepSonar: Towards Effective and Robust Detection of AI-Synthesized Fake Voicescited by 24
- 2019ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworkscited by 23
4 more
- 2020Light Convolutional Neural Network with Feature Genuinization for Detection of Synthetic Speech Attackscited by 23
- 2020Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification: Fundamentalscited by 22
- 2021Channel-wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attackscited by 18
- 2014Audio Splicing Detection and Localization Using Environmental Signaturecited by 2
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