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arXiv · 2207.01052

Generating gender-ambiguous voices for privacy-preserving speech recognition

Dimitrios Stoidis·Andrea Cavallaro
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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.

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Dimitrios Stoidis, Andrea Cavallaro. 2022-07-03. Generating gender-ambiguous voices for privacy-preserving speech recognition. https://arxiv.org/abs/2207.01052

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