  We introduce a novel method for controlling the functionality of a hands-free
speech communication device which comprises a model-based acoustic echo
canceller (AEC), minimum variance distortionless response (MVDR) beamformer
(BF) and spectral postfilter (PF). While the AEC removes the early echo
component, the MVDR BF and PF suppress the residual echo and background noise.
As key innovation, we suggest to use a single deep neural network (DNN) to
jointly control the adaptation of the various algorithmic components. This
allows for rapid convergence and high steady-state performance in the presence
of high-level interfering double-talk. End-to-end training of the DNN using a
time-domain speech extraction loss function avoids the design of individual
control strategies.
