Recently, end-to-end neural diarization (EEND) is introduced and achieves promising results in speaker-overlapped scenarios. In EEND, speaker diarization is formulated as a multi-label prediction problem, where speaker activities are estimated independently and their dependency are not well considered. To overcome these disadvantages, we employ the power set encoding to reformulate speaker diarization as a single-label classification problem and propose the overlap-aware EEND (EEND-OLA) model, in which speaker overlaps and dependency can be modeled explicitly. Inspired by the success of two-stage hybrid systems, we further propose a novel Two-stage OverLap-aware Diarization framework (TOLD) by involving a speaker overlap-aware post-processing (SOAP) model to iteratively refine the diarization results of EEND-OLA. Experimental results show that, compared with the original EEND, the proposed EEND-OLA achieves a 14.39% relative improvement in terms of diarization error rates (DER), and utilizing SOAP provides another 19.33% relative improvement. As a result, our method TOLD achieves a DER of 10.14% on the CALLHOME dataset, which is a new state-of-the-art result on this benchmark to the best of our knowledge.

翻译：近来，端到端神经日志（EEND）方法被引入并在说话人重叠场景中取得了显著成果。在EEND中，说话人日志被建模为多标签预测问题，其中说话人活动被独立估计，且其间的依赖关系未被充分考虑。为克服这些不足，我们采用幂集编码将说话人日志重构为单标签分类问题，并提出重叠感知EEND（EEND-OLA）模型，该模型能够显式建模说话人重叠及其依赖关系。受两阶段混合系统成功经验的启发，我们进一步提出一种新颖的两阶段重叠感知日志框架（TOLD），通过引入说话人重叠感知后处理（SOAP）模型对EEND-OLA的日志结果进行迭代优化。实验结果表明，与原始EEND相比，所提出的EEND-OLA在日志错误率（DER）上实现了14.39%的相对改进，而采用SOAP后额外获得了19.33%的相对提升。最终，我们的TOLD方法在CALLHOME数据集上取得了10.14%的DER，据我们所知，这是该基准测试上的最新最优结果。