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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 &#x2018;expert&#x2019; 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&#x2019;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&#x2019; 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.<sup>1</sup>

Speech Enhancement with Mixture of Deep Experts with Clean Clustering Pre-Training