Protecting Gender and Identity with Disentangled Speech Representations
Protecting Gender and Identity with Disentangled Speech Representations
Besides its linguistic content, our speech is rich in biometric information that can be inferred by classifiers. Learning privacy-preserving representations for speech signals enables downstream tasks without sharing unnecessary, private information about an individual. In this paper, we show that protecting gender information in speech is more effective than modelling …