  This paper proposes a neural network based speech separation method using
spatially distributed microphones. Unlike with traditional microphone array
settings, neither the number of microphones nor their spatial arrangement is
known in advance, which hinders the use of conventional multi-channel speech
separation neural networks based on fixed size input. To overcome this, a novel
network architecture is proposed that interleaves inter-channel processing
layers and temporal processing layers. The inter-channel processing layers
apply a self-attention mechanism along the channel dimension to exploit the
information obtained with a varying number of microphones. The temporal
processing layers are based on a bidirectional long short term memory (BLSTM)
model and applied to each channel independently. The proposed network leverages
information across time and space by stacking these two kinds of layers
alternately. Our network estimates time-frequency (TF) masks for each speaker,
which are then used to generate enhanced speech signals either with TF masking
or beamforming. Speech recognition experimental results show that the proposed
method significantly outperforms baseline multi-channel speech separation
systems.
