  Multichannel processing is widely used for speech enhancement but several
limitations appear when trying to deploy these solutions to the real-world.
Distributed sensor arrays that consider several devices with a few microphones
is a viable alternative that allows for exploiting the multiple devices
equipped with microphones that we are using in our everyday life. In this
context, we propose to extend the distributed adaptive node-specific signal
estimation approach to a neural networks framework. At each node, a local
filtering is performed to send one signal to the other nodes where a mask is
estimated by a neural network in order to compute a global multi-channel Wiener
filter. In an array of two nodes, we show that this additional signal can be
efficiently taken into account to predict the masks and leads to better speech
enhancement performances than when the mask estimation relies only on the local
signals.
