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

Practical applicability of deep neural networks for overlapping speaker separation