# Generating gender-ambiguous voices for privacy-preserving speech recognition

arXiv: https://arxiv.org/abs/2207.01052 · HTML: https://mlsanity.com/paper/2207.01052/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 3 Jul 2022.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis](https://mlsanity.com/paper/1910.06711/) (2019)
- [In defence of metric learning for speaker recognition](https://mlsanity.com/paper/2003.11982/) (2020)
- [QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions](https://mlsanity.com/paper/1910.10261/) (2019)
- [Jasper: An End-to-End Convolutional Neural Acoustic Model](https://mlsanity.com/paper/1904.03288/) (2019)
- [Introducing the VoicePrivacy Initiative](https://mlsanity.com/paper/2005.01387/) (2020)
- [Privacy-preserving Voice Analysis via Disentangled Representations](https://mlsanity.com/paper/2007.15064/) (2020)
- [Defending Your Voice: Adversarial Attack on Voice Conversion](https://mlsanity.com/paper/2005.08781/) (2020)
- [Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?](https://mlsanity.com/paper/1911.04913/) (2019)
- [Privacy-Preserving Adversarial Networks](https://mlsanity.com/paper/1712.07008/) (2017)
- [Adversarial Disentanglement of Speaker Representation for Attribute-Driven Privacy Preservation](https://mlsanity.com/paper/2012.04454/) (2020)
- [Protecting gender and identity with disentangled speech representations](https://mlsanity.com/paper/2104.11051/) (2021)
- [Adversarial representation learning for private speech generation](https://mlsanity.com/paper/2006.09114/) (2020)
- [Adversarial representation learning for synthetic replacement of private attributes](https://mlsanity.com/paper/2006.08039/) (2020)

## Built on by (most cited first, 5 of 5)

- [A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness](https://mlsanity.com/paper/2212.09006/) (2022)
- [MAD Speech: Measures of Acoustic Diversity of Speech](https://mlsanity.com/paper/2404.10419/) (2024)
- [Generating Multilingual Gender-Ambiguous Text-to-Speech Voices](https://mlsanity.com/paper/2211.00375/) (2022)
- [Beyond Neural-on-Neural Approaches to Speaker Gender Protection](https://mlsanity.com/paper/2306.17700/) (2023)
- [Scenario of Use Scheme: Threat Model Specification for Speaker Privacy Protection in the Medical Domain](https://mlsanity.com/paper/2409.16106/) (2024)

## Abstract (arXiv)

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.