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Generating gender-ambiguous voices for privacy-preserving speech recognition

2022-07-03 14:23:02
Dimitrios Stoidis, Andrea Cavallaro

Abstract

Our voice encodes a uniquely identifiable pattern which can be used to infer private attributes, such as gender or identity, that an individual might wish not to reveal when using a speech recognition service. To prevent attribute inference attacks alongside speech recognition tasks, we present a generative adversarial network, GenGAN, that synthesises voices that conceal the gender or identity of a speaker. The proposed network includes a generator with a U-Net architecture that learns to fool a discriminator. We condition the generator only on gender information and use an adversarial loss between signal distortion and privacy preservation. We show that GenGAN improves the trade-off between privacy and utility compared to privacy-preserving representation learning methods that consider gender information as a sensitive attribute to protect.

Abstract (translated)

URL

https://arxiv.org/abs/2207.01052

PDF

https://arxiv.org/pdf/2207.01052.pdf


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