  Spatial clustering techniques can achieve significant multi-channel noise
reduction across relatively arbitrary microphone configurations, but have
difficulty incorporating a detailed speech/noise model. In contrast, LSTM
neural networks have successfully been trained to recognize speech from noise
on single-channel inputs, but have difficulty taking full advantage of the
information in multi-channel recordings. This paper integrates these two
approaches, training LSTM speech models to clean the masks generated by the
Model-based EM Source Separation and Localization (MESSL) spatial clustering
method. By doing so, it attains both the spatial separation performance and
generality of multi-channel spatial clustering and the signal modeling
performance of multiple parallel single-channel LSTM speech enhancers. Our
experiments show that when our system is applied to the CHiME-3 dataset of
noisy tablet recordings, it increases speech quality as measured by the
Perceptual Evaluation of Speech Quality (PESQ) algorithm and reduces the word
error rate of the baseline CHiME-3 speech recognizer, as compared to the
default BeamformIt beamformer.
