  Neural network applications generally benefit from larger-sized models, but
for current speech enhancement models, larger scale networks often suffer from
decreased robustness to the variety of real-world use cases beyond what is
encountered in training data. We introduce several innovations that lead to
better large neural networks for speech enhancement. The novel PoCoNet
architecture is a convolutional neural network that, with the use of
frequency-positional embeddings, is able to more efficiently build
frequency-dependent features in the early layers. A semi-supervised method
helps increase the amount of conversational training data by pre-enhancing
noisy datasets, improving performance on real recordings. A new loss function
biased towards preserving speech quality helps the optimization better match
human perceptual opinions on speech quality. Ablation experiments and objective
and human opinion metrics show the benefits of the proposed improvements.
