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

Phase-Aware Deep Speech Enhancement: It's All About The Frame Length