SpMis: An Investigation of Synthetic Spoken Misinformation Detection
<|reference_start|>SpMis: An Investigation of Synthetic Spoken Misinformation Detection: 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.<|reference_end|>
@article{liu2024spmis:,
    title={SpMis: An Investigation of Synthetic Spoken Misinformation Detection},
    author={Peizhuo Liu, Li Wang, Renqiang He, Haorui He, Lei Wang, Huadi Zheng,
  Jie Shi, Tong Xiao, Zhizheng Wu},
    journal={arXiv preprint arXiv:2409.11308},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11308},
    primaryClass={cs.CL}
}