  Automatic meeting analysis is an essential fundamental technology required to
let, e.g. smart devices follow and respond to our conversations. To achieve an
optimal automatic meeting analysis, we previously proposed an all-neural
approach that jointly solves source separation, speaker diarization and source
counting problems in an optimal way (in a sense that all the 3 tasks can be
jointly optimized through error back-propagation). It was shown that the method
could well handle simulated clean (noiseless and anechoic) dialog-like data,
and achieved very good performance in comparison with several conventional
methods. However, it was not clear whether such all-neural approach would be
successfully generalized to more complicated real meeting data containing more
spontaneously-speaking speakers, severe noise and reverberation, and how it
performs in comparison with the state-of-the-art systems in such scenarios. In
this paper, we first consider practical issues required for improving the
robustness of the all-neural approach, and then experimentally show that, even
in real meeting scenarios, the all-neural approach can perform effective speech
enhancement, and simultaneously outperform state-of-the-art systems.
