  This paper examines the applicability in realistic scenarios of two deep
learning based solutions to the overlapping speaker separation problem.
Firstly, we present experiments that show that these methods are applicable for
a broad range of languages. Further experimentation indicates limited
performance loss for untrained languages, when these have common features with
the trained language(s). Secondly, it investigates how the methods deal with
realistic background noise and proposes some modifications to better cope with
these disturbances. The deep learning methods that will be examined are deep
clustering and deep attractor networks.
