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

Speech enhancement with mixture-of-deep-experts with clean clustering pre-training