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.
View full paper on Arxiv: [here] ISCA Interspeech: [here]
