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

Abstract:

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 A3 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

Recommended citation: Huang, Sung-Feng, Heng-Cheng Kuo, Zhehuai Chen, Xuesong Yang, Chao-Han Huck Yang, Yu Tsao, Yu-Chiang Frank Wang, Hung-yi Lee, and Szu-Wei Fu. "Detecting the Undetectable: Assessing the Efficacy of Current Spoof Detection Methods Against Seamless Speech Edits." In 2024 IEEE Spoken Language Technology Workshop (SLT), pp. 652-659. IEEE, 2024. https://ieeexplore.ieee.org/abstract/document/10832200/