  Frame-online speech enhancement systems in the short-time Fourier transform
(STFT) domain usually have an algorithmic latency equal to the window size due
to the use of overlap-add in the inverse STFT (iSTFT). This algorithmic latency
allows the enhancement models to leverage future contextual information up to a
length equal to the window size. However, this information is only partially
leveraged by current frame-online systems. To fully exploit it, we propose an
overlapped-frame prediction technique for deep learning based frame-online
speech enhancement, where at each frame our deep neural network (DNN) predicts
the current and several past frames that are necessary for overlap-add, instead
of only predicting the current frame. In addition, we propose a loss function
to account for the scale difference between predicted and oracle target
signals. Experiments on a noisy-reverberant speech enhancement task show the
effectiveness of the proposed algorithms.
