LlamaPartialSpoof: An LLM-Driven Fake Speech Dataset Simulating Disinformation Generation
<|reference_start|>LlamaPartialSpoof: An LLM-Driven Fake Speech Dataset Simulating Disinformation Generation: Previous fake speech datasets were constructed from a defender's perspective to develop countermeasure (CM) systems without considering diverse motivations of attackers. To better align with real-life scenarios, we created LlamaPartialSpoof, a 130-hour dataset contains both fully and partially fake speech, using a large language model (LLM) and voice cloning technologies to evaluate the robustness of CMs. By examining information valuable to both attackers and defenders, we identify several key vulnerabilities in current CM systems, which can be exploited to enhance attack success rates, including biases toward certain text-to-speech models or concatenation methods. Our experimental results indicate that current fake speech detection system struggle to generalize to unseen scenarios, achieving a best performance of 24.44% equal error rate.<|reference_end|>
@article{luong2024llamapartialspoof:,
    title={LlamaPartialSpoof: An LLM-Driven Fake Speech Dataset Simulating
  Disinformation Generation},
    author={Hieu-Thi Luong, Haoyang Li, Lin Zhang, Kong Aik Lee and Eng Siong Chng},
    journal={arXiv preprint arXiv:2409.14743},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14743},
    primaryClass={eess.AS cs.SD}
}