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2. Detecting the Undetectable: Assessing the Efficacy of Current Spoof Detection Methods Against Seamless Speech Edits
 
 # Detecting the Undetectable: Assessing the Efficacy of Current Spoof Detection Methods Against Seamless Speech Edits

  ![Publication image](/sites/default/files/styles/wide/public/default_images/default.jpeg?itok=qUFsuJCP "Publication image")

 Neural speech editing advancements have raised concerns about their misuse in spoofing attacks. Traditional partially edited speech corpora primarily focus on cut-and-paste edits, which, while maintaining speaker consistency, often introduce detectable discontinuities. Recent methods, like A^{3}T and Voicebox, improve transitions by leveraging contextual information. To foster spoofing detection research, we introduce the Speech INfilling Edit (SINE) dataset, created with Voicebox. We detailed the process of re-implementing Voicebox training and dataset creation. Subjective evaluations confirm that speech edited using this novel technique is more challenging to detect than conventional cut-and-paste methods. Despite human difficulty, experimental results demonstrate that self-supervised-based detectors can achieve remarkable performance in detection, localization, and generalization across different edit methods. The dataset and related models will be made available at: [https://jasonswfu.github.io/SINE\_dataset/index.html](https://jasonswfu.github.io/SINE_dataset/index.html)



 ## Authors



[Sung-Feng Huang](/index.php/person/sung-feng-huang)

Heng-Cheng Kuo (National Taiwan University)

Zhehuai Chen (NVIDIA)

Xuesong Yang (NVIDIA)

[Huck Yang](/index.php/person/huck-yang)

Yu Tsao (Acedemia Sinica)

[Frank Wang](/index.php/person/frank-wang)

Hung-yi Lee (National Taiwan University)

[Szu-Wei Fu](/index.php/person/szu-wei-fu)

 

 

 ## Publication Date



Monday, December 2, 2024

 

 ## Published in



[IEEE SLT 2024](https://2024.ieeeslt.org/)

 

 ## Research Area



[Artificial Intelligence and Machine Learning ](/index.php/research-area/machine-learning-artificial-intelligence)

[Generative AI](/index.php/research-area/generative-ai)

[Speech Processing](/index.php/research-area/speech-processing)

 

 

 ## External Links



[IEEE Xplore](https://ieeexplore.ieee.org/abstract/document/10832200)