Generating gender-ambiguous voices for privacy-preserving speech recognition
Dimitrios Stoidis, Andrea Cavallaro · Interspeech 2022 · 2022
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 tradeoff between privacy and utility compared to privacy-preserving representation learning methods that consider gender information as a sensitive attribute to protect.