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arXiv · 2409.11308

SpMis: An Investigation of Synthetic Spoken Misinformation Detection

Peizhuo Liu·Li Wang·Renqiang He·Haorui He·Lei Wang·Huadi Zheng·Jie Shi·Tong Xiao·Zhizheng Wu
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Abstract

In recent years, speech generation technology has advanced rapidly, fueled by generative models and large-scale training techniques. While these developments have enabled the production of high-quality synthetic speech, they have also raised concerns about the misuse of this technology, particularly for generating synthetic misinformation. Current research primarily focuses on distinguishing machine-generated speech from human-produced speech, but the more urgent challenge is detecting misinformation within spoken content. This task requires a thorough analysis of factors such as speaker identity, topic, and synthesis. To address this need, we conduct an initial investigation into synthetic spoken misinformation detection by introducing an open-source dataset, SpMis. SpMis includes speech synthesized from over 1,000 speakers across five common topics, utilizing state-of-the-art text-to-speech systems. Although our results show promising detection capabilities, they also reveal substantial challenges for practical implementation, underscoring the importance of ongoing research in this critical area.

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Peizhuo Liu, Li Wang, Renqiang He, Haorui He, Lei Wang, Huadi Zheng, Jie Shi, Tong Xiao, Zhizheng Wu. 2024-09-17. SpMis: An Investigation of Synthetic Spoken Misinformation Detection. https://arxiv.org/abs/2409.11308

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