  Deep learning based speech enhancement has made rapid development towards
improving quality, while models are becoming more compact and usable for
real-time on-the-edge inference. However, the speech quality scales directly
with the model size, and small models are often still unable to achieve
sufficient quality. Furthermore, the introduced speech distortion and artifacts
greatly harm speech quality and intelligibility, and often significantly
degrade automatic speech recognition (ASR) rates. In this work, we shed light
on the success of the spectral complex compressed mean squared error (MSE)
loss, and how its magnitude and phase-aware terms are related to the speech
distortion vs. noise reduction trade off. We further investigate integrating
pre-trained reference-less predictors for mean opinion score (MOS) and word
error rate (WER), and pre-trained embeddings on ASR and sound event detection.
Our analyses reveal that none of the pre-trained networks added significant
performance over the strong spectral loss.
