Detecting the Undetectable: Assessing the Efficacy of Current Spoof Detection Methods Against Seamless Speech Edits
Part Of
Proceedings of 2024 IEEE Spoken Language Technology Workshop, SLT 2024
Start Page
652
End Page
659
ISBN
979-835039225-8
Date Issued
2024-12-02
Author(s)
Sung-Feng Huang
Heng-Cheng Kuo
Zhehuai Chen
Xuesong Yang
Chao-Han Huck Yang
Yu Tsao
Szu-Wei Fu
DOI
10.1109/SLT61566.2024.10832200
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 A3T 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.
Event(s)
2024 IEEE Spoken Language Technology Workshop, SLT 2024
Publisher
IEEE
Type
conference paper