Generating gender-ambiguous voices for privacy-preserving speech recognition

• URL: http://arxiv.org/abs/2207.01052v1
• Date: Sun, 3 Jul 2022 14:23:02 GMT
• Title: Generating gender-ambiguous voices for privacy-preserving speech recognition
• Authors: Dimitrios Stoidis and Andrea Cavallaro
• Abstract summary: We present a generative adversarial network, GenGAN, that synthesises voices that conceal the gender or identity of a speaker. We condition the generator only on gender information and use an adversarial loss between signal distortion and privacy preservation.
• Score: 38.733077459065704
• License: http://creativecommons.org/licenses/by/4.0/
• 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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