Beyond Neural-on-Neural Approaches to Speaker Gender Protection
Loes van Bemmel Zhuoran Liu Nik Vaessen Martha Larson
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.