  Recurrent neural networks using the LSTM architecture can achieve significant
single-channel noise reduction. It is not obvious, however, how to apply them
to multi-channel inputs in a way that can generalize to new microphone
configurations. In contrast, spatial clustering techniques can achieve such
generalization, but lack a strong signal model. This paper combines the two
approaches to attain both the spatial separation performance and generality of
multichannel spatial clustering and the signal modeling performance of multiple
parallel single-channel LSTM speech enhancers. The system is compared to
several baselines on the CHiME3 dataset in terms of speech quality predicted by
the PESQ algorithm and word error rate of a recognizer trained on mis-matched
conditions, in order to focus on generalization. Our experiments show that by
combining the LSTM models with the spatial clustering, we reduce word error
rate by 4.6\% absolute (17.2\% relative) on the development set and 11.2\%
absolute (25.5\% relative) on test set compared with spatial clustering system,
and reduce by 10.75\% (32.72\% relative) on development set and 6.12\% absolute
(15.76\% relative) on test data compared with LSTM model.
