  In this study we present a mixture of deep experts (MoDE) neural-network
architecture for single microphone speech enhancement. Our architecture
comprises a set of deep neural networks (DNNs), each of which is an 'expert' in
a different speech spectral pattern such as phoneme. A gating DNN is
responsible for the latent variables which are the weights assigned to each
expert's output given a speech segment. The experts estimate a mask from the
noisy input and the final mask is then obtained as a weighted average of the
experts' estimates, with the weights determined by the gating DNN. A soft
spectral attenuation, based on the estimated mask, is then applied to enhance
the noisy speech signal. As a byproduct, we gain reduction at the complexity in
test time. We show that the experts specialization allows better robustness to
unfamiliar noise types.
