  Voice activity and overlapped speech detection (respectively VAD and OSD) are
key pre-processing tasks for speaker diarization. The final segmentation
performance highly relies on the robustness of these sub-tasks. Recent studies
have shown VAD and OSD can be trained jointly using a multi-class
classification model. However, these works are often restricted to a specific
speech domain, lacking information about the generalization capacities of the
systems. This paper proposes a complete and new benchmark of different VAD and
OSD models, on multiple audio setups (single/multi-channel) and speech domains
(e.g. media, meeting...). Our 2/3-class systems, which combine a Temporal
Convolutional Network with speech representations adapted to the setup,
outperform state-of-the-art results. We show that the joint training of these
two tasks offers similar performances in terms of F1-score to two dedicated VAD
and OSD systems while reducing the training cost. This unique architecture can
also be used for single and multichannel speech processing.
