  Subjective evaluation results for two low-latency deep neural networks (DNN)
are compared to a matured version of a traditional Wiener-filter based noise
suppressor. The target use-case is real-world single-channel speech enhancement
applications, e.g., communications. Real-world recordings consisting of
additive stationary and non-stationary noise types are included. The evaluation
is divided into four outcomes: speech quality, noise transparency, speech
intelligibility or listening effort, and noise level w.r.t. speech. It is shown
that DNNs improve noise suppression in all conditions in comparison to the
traditional Wiener-filter baseline without major degradation in speech quality
and noise transparency while maintaining speech intelligibility better than the
baseline.
