  Impressive progress in neural network-based single-channel speech source
separation has been made in recent years. But those improvements have been
mostly reported on anechoic data, a situation that is hardly met in practice.
Taking the SepFormer as a starting point, which achieves state-of-the-art
performance on anechoic mixtures, we gradually modify it to optimize its
performance on reverberant mixtures. Although this leads to a word error rate
improvement by 7 percentage points compared to the standard SepFormer
implementation, the system ends up with only marginally better performance than
a PIT-BLSTM separation system, that is optimized with rather straightforward
means. This is surprising and at the same time sobering, challenging the
practical usefulness of many improvements reported in recent years for monaural
source separation on nonreverberant data.
