  To date, the bulk of research on single-channel speech separation has been
conducted using clean, near-field, read speech, which is not representative of
many modern applications. In this work, we develop a procedure for constructing
high-quality synthetic overlap datasets, necessary for most deep learning-based
separation frameworks. We produced datasets that are more representative of
realistic applications using the CHiME-5 and Mixer 6 corpora and evaluate
standard methods on this data to demonstrate the shortcomings of current
source-separation performance. We also demonstrate the value of a wide variety
of data in training robust models that generalize well to multiple conditions.
