Voice activity and overlapped speech detection (respectively VAD and OSD) are key pre-processing tasks for speaker diarization. The final segmentation performance highly relies on the robustness of these sub-tasks. Recent studies have shown VAD and OSD can be trained jointly using a multi-class classification model. However, these works are often restricted to a specific speech domain, lacking information about the generalization capacities of the systems. This paper proposes a complete and new benchmark of different VAD and OSD models, on multiple audio setups (single/multi-channel) and speech domains (e.g. media, meeting...). Our 2/3-class systems, which combine a Temporal Convolutional Network with speech representations adapted to the setup, outperform state-of-the-art results. We show that the joint training of these two tasks offers similar performances in terms of F1-score to two dedicated VAD and OSD systems while reducing the training cost. This unique architecture can also be used for single and multichannel speech processing.

声活动和重叠演讲检测对于说话人分析是关键的预处理任务。本文提出了一个全面的新绩效基准，用于多种音频设置（单/多通道）和语音领域上的不同声活动和重叠演讲检测模型，并展示了联合训练这两项任务的独特架构在降低训练成本的同时能够获得与两个专门的声活动和重叠演讲检测系统相似的F1分数表现。

联合语音与重叠检测：多个音频设置和语音领域的基准测试