Advancing the study of Large-Scale Learning in Overlapped Speech Detection

Overlapped Speech Detection (OSD) is an important part of speech applications involving analysis of multi-party conversations. However, most of the existing OSD systems are trained and evaluated on specific dataset, which limits the application scenarios of these systems. To solve this problem, we conduct a study of large-scale learning (LSL) in OSD tasks and propose a general 16K single-channel OSD system. In our study, 522 hours of labeled audio in different languages and styles are collected and used as the large-scale dataset. Rigorous comparative experiments are designed and used to evaluate the effectiveness of LSL in OSD tasks and select the appropriate model of general OSD system. The results show that LSL can significantly improve the performance and robustness of OSD models, and the OSD model based on Conformer (CF-OSD) with LSL is currently the best 16K single-channel OSD system. Moreover, the CF-OSD with LSL establishes a state-of-the-art performance with an F1-score of 81.6% and 53.8% on Alimeeting test set and DIHARD II evaluation set, respectively.