  Overlapped Speech Detection (OSD) is an important part of speech applications
involving analysis of multi-party conversations. However, most of existing OSD
systems are trained and evaluated on small datasets with limited application
domains, which led to the robustness of them lacks benchmark for evaluation and
the accuracy of them remains inadequate in realistic acoustic environments. To
solve these problem, we conduct a study of large-scale learning (LSL) in OSD
tasks and propose a new general OSD system named CF-OSD with LSL based on
Conformer network and LSL. In our study, a large-scale test set consisting of
151h labeled speech of different styles, languages and sound-source distances
is produced and used as a new benchmark for evaluating the generality of OSD
systems. Rigorous comparative experiments are designed and used to evaluate the
effectiveness of LSL in OSD tasks and define the OSD model of our general OSD
system. The experiment results show that LSL can significantly improve the
accuracy and robustness of OSD systems, and the CF-OSD with LSL system
significantly outperforms other OSD systems on our proposed benchmark.
Moreover, our system has also achieved state-of-the-art performance on existing
small dataset benchmarks, reaching 81.6\% and 53.8\% in the Alimeeting testset
and DIHARD II evaluation set, respectively.
