  In this paper, we demonstrate a method for training speaker embedding
extractors using weak annotation. More specifically, we are using the full
VoxCeleb recordings and the name of the celebrities appearing on each video
without knowledge of the time intervals the celebrities appear in the video. We
show that by combining a baseline speaker diarization algorithm that requires
no training or parameter tuning, a modified loss with aggregation over
segments, and a two-stage training approach, we are able to train a competitive
ResNet-based embedding extractor. Finally, we experiment with two different
aggregation functions and analyze their behaviour in terms of their gradients.
