  We propose a neural network-based speech enhancement (SE) method called the
phase-aware recurrent two stage network (rTSN). The rTSN is an extension of our
previously proposed two stage network (TSN) framework. This TSN framework was
equipped with a boosting strategy (BS) that initially estimates the multiple
base predictions (MBPs) from a prior neural network (pri-NN) and then the MBPs
are aggregated by a posterior neural network (post-NN) to obtain the final
prediction. The TSN outperformed various state-of-the-art methods; however, it
adopted the simple deep neural network as pri-NN. We have found that the pri-NN
affects the performance (in perceptual quality), more than post-NN; therefore
we adopted the long short-term memory recurrent neural network (LSTM-RNN) as
pri-NN to boost the context information usage within speech signals. Further,
the TSN framework did not consider the phase reconstruction, though phase
information affected the perceptual quality. Therefore, we proposed to adopt
the phase reconstruction method based on the Griffin-Lim algorithm. Finally, we
evaluated rTSN with baselines such as TSN in perceptual quality related metrics
as well as the phone recognition error rate.
