Active Speaker Detection

Active speaker detection (ASD) aims to identify who is speaking in a video, a crucial task for applications like video conferencing, human-robot interaction, and automatic video editing. Current research emphasizes improving the robustness and efficiency of ASD systems, particularly in noisy environments and with multiple speakers, often employing deep learning models such as transformers, convolutional recurrent neural networks (CRNNs), and graph neural networks (GNNs) that integrate audio and visual information. These advancements are driving progress in areas like real-time processing, handling occlusions and off-screen speakers, and improving accuracy across diverse datasets and challenging conditions, ultimately impacting fields ranging from assistive technologies to media analysis.