Practical applicability of deep neural networks for overlapping speaker separation

This paper examines the applicability in realistic scenari os of two deep learning based solutions to the overlapping speake r separation problem. Firstly, we present experiments that s how that these methods are applicable for a broad range of languages. Further experimentation indicates limited perfor mance loss for untrained languages, when these have common features with the trained language(s). Secondly, it investiga tes how the methods deal with realistic background noise and propos es some modifications to better cope with these disturbances. T he deep learning methods that will be examined are deep cluster ing and deep attractor networks.