# Beyond Neural-on-Neural Approaches to Speaker Gender Protection

arXiv: https://arxiv.org/abs/2306.17700 · HTML: https://mlsanity.com/paper/2306.17700/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 30 Jun 2023.

## Builds on (most cited first, 16 of 16)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [On Adaptive Attacks to Adversarial Example Defenses](https://mlsanity.com/paper/2002.08347/) (2020)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [Very Deep Convolutional Neural Networks for Raw Waveforms](https://mlsanity.com/paper/1610.00087/) (2016)
- [Introducing the VoicePrivacy Initiative](https://mlsanity.com/paper/2005.01387/) (2020)
- [Crafting Adversarial Examples For Speech Paralinguistics Applications](https://mlsanity.com/paper/1711.03280/) (2017)
- [Privacy-preserving Voice Analysis via Disentangled Representations](https://mlsanity.com/paper/2007.15064/) (2020)
- [Adversarial Disentanglement of Speaker Representation for Attribute-Driven Privacy Preservation](https://mlsanity.com/paper/2012.04454/) (2020)
- [A Robust Approach for Securing Audio Classification Against Adversarial Attacks](https://mlsanity.com/paper/1904.10990/) (2019)
- [Overlapped speech and gender detection with WavLM pre-trained features](https://mlsanity.com/paper/2209.04167/) (2022)
- [Understanding the Tradeoffs in Client-side Privacy for Downstream Speech Tasks](https://mlsanity.com/paper/2101.08919/) (2021)
- [Protecting gender and identity with disentangled speech representations](https://mlsanity.com/paper/2104.11051/) (2021)
- [Generating gender-ambiguous voices for privacy-preserving speech recognition](https://mlsanity.com/paper/2207.01052/) (2022)

## Abstract (arXiv)

Recent research has proposed approaches that modify speech to defend against gender inference attacks. The goal of these protection algorithms is to control the availability of information about a speaker's gender, a privacy-sensitive attribute. Currently, the common practice for developing and testing gender protection algorithms is "neural-on-neural", i.e., perturbations are generated and tested with a neural network. In this paper, we propose to go beyond this practice to strengthen the study of gender protection. First, we demonstrate the importance of testing gender inference attacks that are based on speech features historically developed by speech scientists, alongside the conventionally used neural classifiers. Next, we argue that researchers should use speech features to gain insight into how protective modifications change the speech signal. Finally, we point out that gender-protection algorithms should be compared with novel "vocal adversaries", human-executed voice adaptations, in order to improve interpretability and enable before-the-mic protection.