  With recent research advancements, deep learning models are becoming
attractive and powerful choices for speech enhancement in real-time
applications. While state-of-the-art models can achieve outstanding results in
terms of speech quality and background noise reduction, the main challenge is
to obtain compact enough models, which are resource efficient during inference
time. An important but often neglected aspect for data-driven methods is that
results can be only convincing when tested on real-world data and evaluated
with useful metrics. In this work, we investigate reasonably small recurrent
and convolutional-recurrent network architectures for speech enhancement,
trained on a large dataset considering also reverberation. We show interesting
tradeoffs between computational complexity and the achievable speech quality,
measured on real recordings using a highly accurate MOS estimator. It is shown
that the achievable speech quality is a function of network complexity, and
show which models have better tradeoffs.
