Generating gender-ambiguous voices for privacy-preserving speech recognition - Dimitrios Stoidis
Generating gender-ambiguous voices for privacy-preserving speech recognition
Dimitrios Stoidis, Andrea Cavallaro
Centre for Intelligent Sensing, Queen Mary University of London (QMUL), U.K.
Presented at INTERSPEECH 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 trade-off between privacy and utility compared to privacy-preserving representation learning methods that consider gender information as a sensitive attribute to protect.
Official paper: http://doi.org/10.21437/Interspeech.2...
ArXiv: https://doi.org/10.48550/arXiv.2207.0...
Code: https://github.com/dimitriStoidis/GenGAN