  Enhancing noisy speech is an important task to restore its quality and to
improve its intelligibility. In traditional non-machine-learning (ML) based
approaches the parameters required for noise reduction are estimated blindly
from the noisy observation while the actual filter functions are derived
analytically based on statistical assumptions. Even though such approaches
generalize well to many different acoustic conditions, the noise suppression
capability in transient noises is low. To amend this shortcoming,
machine-learning (ML) methods such as deep learning have been employed for
speech enhancement. However, due to their data-driven nature, the
generalization of ML based approaches to unknown noise types is still
discussed. To improve the generalization of ML based algorithms and to enhance
the noise suppression of non-ML based methods, we propose a combination of both
approaches. For this, we employ the a priori signal-to-noise ratio (SNR) and
the a posteriori SNR estimated as input features in a deep neural network (DNN)
based enhancement scheme. We show that this approach allows ML based speech
estimators to generalize quickly to unknown noise types even if only few noise
conditions have been seen during training. Further, the proposed features
outperform a competing approach where an estimate of the noise power spectral
density is appended to the noisy spectra. Instrumental measures such as
Perceptual Evaluation of Speech Quality (PESQ) and short-time objective
intelligibility (STOI) indicate strong improvements in unseen conditions when
the proposed features are used. Listening experiments confirm the improved
generalization of our proposed combination.
