  Recently, phase processing is attracting increasinginterest in speech
enhancement community. Some researchersintegrate phase estimations module into
speech enhancementmodels by using complex-valued short-time Fourier
transform(STFT) spectrogram based training targets, e.g. Complex RatioMask
(cRM) [1]. However, masking on spectrogram would violentits consistency
constraints. In this work, we prove that theinconsistent problem enlarges the
solution space of the speechenhancement model and causes unintended artifacts.
ConsistencySpectrogram Masking (CSM) is proposed to estimate the
complexspectrogram of a signal with the consistency constraint in asimple but
not trivial way. The experiments comparing ourCSM based end-to-end model with
other methods are conductedto confirm that the CSM accelerate the model
training andhave significant improvements in speech quality. From
ourexperimental results, we assured that our method could enha
