  Single-channel speech separation is a crucial task for enhancing speech
recognition systems in multi-speaker environments. This paper investigates the
robustness of state-of-the-art Neural Network models in scenarios where the
pitch differences between speakers are minimal. Building on earlier findings by
Ditter and Gerkmann, which identified a significant performance drop for the
2018 Chimera++ under similar-pitch conditions, our study extends the analysis
to more recent and sophisticated Neural Network models. Our experiments reveal
that modern models have substantially reduced the performance gap for matched
training and testing conditions. However, a substantial performance gap
persists under mismatched conditions, with models performing well for large
pitch differences but showing worse performance if the speakers' pitches are
similar. These findings motivate further research into the generalizability of
speech separation models to similar-pitch speakers and unseen data.
