HLTCOE JHU Submission to the Voice Privacy Challenge 2024
<|reference_start|>HLTCOE JHU Submission to the Voice Privacy Challenge 2024: We present a number of systems for the Voice Privacy Challenge, including voice conversion based systems such as the kNN-VC method and the WavLM voice Conversion method, and text-to-speech (TTS) based systems including Whisper-VITS. We found that while voice conversion systems better preserve emotional content, they struggle to conceal speaker identity in semi-white-box attack scenarios; conversely, TTS methods perform better at anonymization and worse at emotion preservation. Finally, we propose a random admixture system which seeks to balance out the strengths and weaknesses of the two category of systems, achieving a strong EER of over 40% while maintaining UAR at a respectable 47%.<|reference_end|>
@article{xinyuan2024hltcoe,
    title={HLTCOE JHU Submission to the Voice Privacy Challenge 2024},
    author={Henry Li Xinyuan, Zexin Cai, Ashi Garg, Kevin Duh, Leibny Paola
  Garc'ia-Perera, Sanjeev Khudanpur, Nicholas Andrews, Matthew Wiesner},
    journal={arXiv preprint arXiv:2409.08913},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08913},
    primaryClass={eess.AS cs.LG}
}