构建首个合成语音假信息检测数据集，助力识别虚假语音内容
SpMis: An Investigation of Synthetic Spoken Misinformation Detection
- 构建包含1000+说话人、5类话题的合成语音数据集SpMis
- 利用先进语音合成技术生成高仿真语音，验证检测模型可行性
- 为防范语音诈骗提供基础研究支持，适合安全与可信AI研究者
近年来，语音生成技术在生成模型和大规模训练技术推动下迅速发展。尽管这提升了合成语音质量，但也带来了滥用风险，尤其是生成合成虚假信息。现有研究多聚焦于区分机器与人类语音，但更紧迫的问题是检测语音内容中的虚假信息。该任务需综合分析说话人身份、话题和合成方式等因素。为此，本文首次开展合成语音假信息检测研究，提出开源数据集SpMis，涵盖超过1000名说话人、五类常见话题，采用前沿文本转语音系统生成语音。实验显示检测具备潜力，但仍面临实际应用挑战，凸显该领域持续研究的重要性。
原文摘要 · Abstract (English)
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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