Protecting gender and identity with disentangled speech representations - Dimitrios Stoidis
2021 Intelligent Sensing Winter School
Protecting gender and identity with disentangled speech representations
Dimitrios Stoidis, Queen Mary University of London
Besides its linguistic component, our speech is rich in biometric information that can be inferred by classifiers. Learning privacy-preserving representations for speech signals enables downstream tasks, such as speech recognition, without sharing unnecessary private information about an individual. In this short presentation we will show how gender recognition and speaker verification tasks can be reduced to a random guess, protecting against classification-based attacks.
Slides: http://cis.eecs.qmul.ac.uk/2021Winter...
As part of the 2021 Intelligent Sensing Winter School: http://cis.eecs.qmul.ac.uk/school2021...