Who said that?: Audio-visual speaker diarisation of real-world meetings
This work addresses speaker diarisation for real-world meetings, which is an incremental improvement with strong specific gains in performance.
The paper tackles the problem of determining 'who spoke when' in real-world meetings by proposing an iterative audio-visual method that enrolls speaker models and identifies active speakers, achieving results that exceed all comparable methods on the AMI meeting corpus.
The goal of this work is to determine 'who spoke when' in real-world meetings. The method takes surround-view video and single or multi-channel audio as inputs, and generates robust diarisation outputs. To achieve this, we propose a novel iterative approach that first enrolls speaker models using audio-visual correspondence, then uses the enrolled models together with the visual information to determine the active speaker. We show strong quantitative and qualitative performance on a dataset of real-world meetings. The method is also evaluated on the public AMI meeting corpus, on which we demonstrate results that exceed all comparable methods. We also show that beamforming can be used together with the video to further improve the performance when multi-channel audio is available.