  Information on speaker characteristics can be useful as side information in
improving speaker recognition accuracy. However, such information is often
private. This paper investigates how privacy-preserving learning can improve a
speaker verification system, by enabling the use of privacy-sensitive speaker
data to train an auxiliary classification model that predicts vocal
characteristics of speakers. In particular, this paper explores the utility
achieved by approaches which combine different federated learning and
differential privacy mechanisms. These approaches make it possible to train a
central model while protecting user privacy, with users' data remaining on
their devices. Furthermore, they make learning on a large population of
speakers possible, ensuring good coverage of speaker characteristics when
training a model. The auxiliary model described here uses features extracted
from phrases which trigger a speaker verification system. From these features,
the model predicts speaker characteristic labels considered useful as side
information. The knowledge of the auxiliary model is distilled into a speaker
verification system using multi-task learning, with the side information labels
predicted by this auxiliary model being the additional task. This approach
results in a 6% relative improvement in equal error rate over a baseline
system.
