  Despite the remarkable progress recently made in distant speech recognition,
state-of-the-art technology still suffers from a lack of robustness, especially
when adverse acoustic conditions characterized by non-stationary noises and
reverberation are met. A prominent limitation of current systems lies in the
lack of matching and communication between the various technologies involved in
the distant speech recognition process. The speech enhancement and speech
recognition modules are, for instance, often trained independently. Moreover,
the speech enhancement normally helps the speech recognizer, but the output of
the latter is not commonly used, in turn, to improve the speech enhancement. To
address both concerns, we propose a novel architecture based on a network of
deep neural networks, where all the components are jointly trained and better
cooperate with each other thanks to a full communication scheme between them.
Experiments, conducted using different datasets, tasks and acoustic conditions,
revealed that the proposed framework can overtake other competitive solutions,
including recent joint training approaches.
