  Algorithmic latency in speech processing is dominated by the frame length
used for Fourier analysis, which in turn limits the achievable performance of
magnitude-centric approaches. As previous studies suggest the importance of
phase grows with decreasing frame length, this work presents a systematical
study on the contribution of phase and magnitude in modern Deep Neural Network
(DNN)-based speech enhancement at different frame lengths. Results indicate
that DNNs can successfully estimate phase when using short frames, with similar
or better overall performance compared to using longer frames. Thus,
interestingly, modern phase-aware DNNs allow for low-latency speech enhancement
at high quality.
