  Recently, a generative variational autoencoder (VAE) has been proposed for
speech enhancement to model speech statistics. However, this approach only uses
clean speech in the training phase, making the estimation particularly
sensitive to noise presence, especially in low signal-to-noise ratios (SNRs).
To increase the robustness of the VAE, we propose to include noise information
in the training phase by using a noise-aware encoder trained on noisy-clean
speech pairs. We evaluate our approach on real recordings of different noisy
environments and acoustic conditions using two different noise datasets. We
show that our proposed noise-aware VAE outperforms the standard VAE in terms of
overall distortion without increasing the number of model parameters. At the
same time, we demonstrate that our model is capable of generalizing to unseen
noise conditions better than a supervised feedforward deep neural network
(DNN). Furthermore, we demonstrate the robustness of the model performance to a
reduction of the noisy-clean speech training data size.
