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arxiv: 2409.08913 · v2 · pith:EAUDONAR · submitted 2024-09-13 · eess.AS · cs.LG

HLTCOE JHU Submission to the Voice Privacy Challenge 2024

Henry Li Xinyuan , Zexin Cai , Ashi Garg , Kevin Duh , Leibny Paola Garc\'ia-Perera , Sanjeev Khudanpur , Nicholas Andrews , Matthew Wiesner This is my paper

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classification eess.AS cs.LG
keywords systemsvoiceconversionbetterchallengeincludingmethodprivacy
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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%.

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