  Recent works have shown that Deep Recurrent Neural Networks using the LSTM
architecture can achieve strong single-channel speech enhancement by estimating
time-frequency masks. However, these models do not naturally generalize to
multi-channel inputs from varying microphone configurations. In contrast,
spatial clustering techniques can achieve such generalization but lack a strong
signal model. Our work proposes a combination of the two approaches. By using
LSTMs to enhance spatial clustering based time-frequency masks, we achieve both
the signal modeling performance of multiple single-channel LSTM-DNN speech
enhancers and the signal separation performance and generality of multi-channel
spatial clustering. We compare our proposed system to several baselines on the
CHiME-3 dataset. We evaluate the quality of the audio from each system using
SDR from the BSS\_eval toolkit and PESQ. We evaluate the intelligibility of the
output of each system using word error rate from a Kaldi automatic speech
recognizer.
