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Overlapped Speech Detection (OSD) is an important part of speech applicationsinvolving analysis of multi-party conversations. However, most of existing OSDsystems are trained and evaluated on small datasets with limited applicationdomains, which led to the robustness of them lacks benchmark for evaluation andthe accuracy of them remains inadequate in realistic acoustic environments. Tosolve these problem, we conduct a study of large-scale learning (LSL) in OSDtasks and propose a new general OSD system named CF-OSD with LSL based onConformer network and LSL. In our study, a large-scale test set consisting of151h labeled speech of different styles, languages and sound-source distancesis produced and used as a new benchmark for evaluating the generality of OSDsystems. Rigorous comparative experiments are designed and used to evaluate theeffectiveness of LSL in OSD tasks and define the OSD model of our general OSDsystem. The experiment results show that LSL can significantly improve theaccuracy and robustness of OSD systems, and the CF-OSD with LSL systemsignificantly outperforms other OSD systems on our proposed benchmark.Moreover, our system has also achieved state-of-the-art performance on existingsmall dataset benchmarks, reaching 81.6\% and 53.8\% in the Alimeeting testsetand DIHARD II evaluation set, respectively.

Large-Scale Learning on Overlapped Speech Detection: New Benchmark and New General System