  We propose using an adversarial autoencoder (AAE) to replace generative
adversarial network (GAN) in the private aggregation of teacher ensembles
(PATE), a solution for ensuring differential privacy in speech applications.
The AAE architecture allows us to obtain good synthetic speech leveraging upon
a discriminative training of latent vectors. Such synthetic speech is used to
build a privacy-preserving classifier when non-sensitive data is not
sufficiently available in the public domain. This classifier follows the PATE
scheme that uses an ensemble of noisy outputs to label the synthetic samples
and guarantee $\varepsilon$-differential privacy (DP) on its derived
classifiers.
  Our proposed framework thus consists of an AAE-based generator and a
PATE-based classifier (PATE-AAE). Evaluated on the Google Speech Commands
Dataset Version II, the proposed PATE-AAE improves the average classification
accuracy by +$2.11\%$ and +$6.60\%$, respectively, when compared with
alternative privacy-preserving solutions, namely PATE-GAN and DP-GAN, while
maintaining a strong level of privacy target at $\varepsilon$=0.01 with a fixed
$\delta$=10$^{-5}$.
