  In this paper, we address the generalization of deep neural network (DNN)
based speech enhancement to unseen noise conditions for the case that training
data is limited in size and diversity. To gain more insights, we analyze the
generalization with respect to (1) the size and diversity of the training data,
(2) different network architectures, and (3) the chosen features. To address
(1), we train networks on the Hu noise corpus (limited size), the CHiME 3 noise
corpus (limited diversity) and also propose a large and diverse dataset
collected based on freely available sounds. To address (2), we compare a
fully-connected feed-forward and a long short-term memory (LSTM) architecture.
To address (3), we compare three input features, namely logarithmized noisy
periodograms, noise aware training (NAT) and the proposed signal-to-noise ratio
(SNR) based noise aware training (SNR-NAT). We confirm that rich training data
and improved network architectures help DNNs to generalize. Furthermore, we
show via experimental results and an analysis using t-distributed stochastic
neighbor embedding (t-SNE) that the proposed SNR-NAT features yield robust and
level independent results in unseen noise even with simple network
architectures and when trained on only small datasets, which is the key
contribution of this paper.
