  Because the performance of speech separation is excellent for speech in which
two speakers completely overlap, research attention has been shifted to dealing
with more realistic scenarios. However, domain mismatch between training/test
situations due to factors, such as speaker, content, channel, and environment,
remains a severe problem for speech separation. Speaker and environment
mismatches have been studied in the existing literature. Nevertheless, there
are few studies on speech content and channel mismatches. Moreover, the impacts
of language and channel in these studies are mostly tangled. In this study, we
create several datasets for various experiments. The results show that the
impacts of different languages are small enough to be ignored compared to the
impacts of different channels. In our experiments, training on data recorded by
Android phones leads to the best generalizability. Moreover, we provide a new
solution for channel mismatch by evaluating projection, where the channel
similarity can be measured and used to effectively select additional training
data to improve the performance of in-the-wild test data.
