  Recently, variational autoencoders have been successfully used to learn a
probabilistic prior over speech signals, which is then used to perform speech
enhancement. However, variational autoencoders are trained on clean speech
only, which results in a limited ability of extracting the speech signal from
noisy speech compared to supervised approaches. In this paper, we propose to
guide the variational autoencoder with a supervised classifier separately
trained on noisy speech. The estimated label is a high-level categorical
variable describing the speech signal (e.g. speech activity) allowing for a
more informed latent distribution compared to the standard variational
autoencoder. We evaluate our method with different types of labels on real
recordings of different noisy environments. Provided that the label better
informs the latent distribution and that the classifier achieves good
performance, the proposed approach outperforms the standard variational
autoencoder and a conventional neural network-based supervised approach.
