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- W4319862433 abstract "In order to protect the privacy of speech data, speaker anonymization aims for hiding the identity of a speaker by changing the voice in speech recordings. This typically comes with a privacy-utility trade-off between protection of individuals and usability of the data for downstream applications. One of the challenges in this context is to create non-existent voices that sound as natural as possible. In this work, we propose to tackle this issue by generating speaker embeddings using a generative adversarial network with Wasserstein distance as cost function. By incorporating these artificial embeddings into a speech-to-text-to-speech pipeline, we outperform previous approaches in terms of privacy and utility. According to standard objective metrics and human evaluation, our approach generates intelligible and content-preserving yet privacy-protecting versions of the original recordings." @default.
- W4319862433 created "2023-02-11" @default.
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- W4319862433 date "2023-01-09" @default.
- W4319862433 modified "2023-09-27" @default.
- W4319862433 title "Anonymizing Speech with Generative Adversarial Networks to Preserve Speaker Privacy" @default.
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- W4319862433 doi "https://doi.org/10.1109/slt54892.2023.10022601" @default.
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