Beyond Neural-on-Neural Approaches to Speaker Gender Protection

Author:

Loes van Bemmel, Zhuoran Liu, Nik Vaessen, Martha Larson

Keyword:

Electrical Engineering and Systems Science, Audio and Speech Processing, Audio and Speech Processing (eess.AS), Computation and Language (cs.CL), Cryptography and Security (cs.CR), Computers and Society (cs.CY), Machine Learning (cs.LG), Sound (cs.SD)

journal:

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023)

date:

2023-06-29 16:00:00

Abstract

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.

PDF: Beyond Neural-on-Neural Approaches to Speaker Gender Protection.pdf