Speaker Tracking

Speaker tracking aims to identify and follow the location of speakers over time, leveraging audio and/or visual information. Current research emphasizes robust methods for fusing audio and visual data, often employing deep learning architectures like transformers and recurrent neural networks, to improve accuracy and handle challenging conditions such as noise, occlusion, and multiple speakers. These advancements are crucial for applications ranging from autonomous driving and assistive technologies to improving human-computer interaction and analyzing social dynamics. The development of large-scale benchmark datasets and lightweight models is also a significant focus, enabling broader accessibility and deployment of speaker tracking systems.