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 models are trained and evaluated on specific dataset, which limits the application scenarios of these models. In order to solve this problem, we conduct a study of large-scale learning (LSL) in OSD and propose a more general 16K single-channel OSD model. 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 task and the performance of OSD models based on different deep neural networks. 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 model. Moreover, the CF-OSD with LSL establishes a state-of-the-art performance with a F1-score of 80.8% and 52.0% on the Alimeeting test set and DIHARD II evaluation set, respectively.