  Modern neural speech enhancement models usually include various forms of
phase information in their training loss terms, either explicitly or
implicitly. However, these loss terms are typically designed to reduce the
distortion of phase spectrum values at specific frequencies, which ensures they
do not significantly affect the quality of the enhanced speech. In this paper,
we propose an effective phase reconstruction strategy for neural speech
enhancement that can operate in noisy environments. Specifically, we introduce
a phase continuity loss that considers relative phase variations across the
time and frequency axes. By including this phase continuity loss in a
state-of-the-art neural speech enhancement system trained with reconstruction
loss and a number of magnitude spectral losses, we show that our proposed
method further improves the quality of enhanced speech signals over the
baseline, especially when training is done jointly with a magnitude spectrum
loss.
