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In this paper, we demonstrate a method for training speaker embeddingextractors using weak annotation. More specifically, we are using the fullVoxCeleb recordings and the name of the celebrities appearing on each videowithout knowledge of the time intervals the celebrities appear in the video. Weshow that by combining a baseline speaker diarization algorithm that requiresno training or parameter tuning, a modified loss with aggregation oversegments, and a two-stage training approach, we are able to train a competitiveResNet-based embedding extractor. Finally, we experiment with two differentaggregation functions and analyze their behaviour in terms of their gradients.

Training Speaker Embedding Extractors Using Multi-Speaker Audio with Unknown Speaker Boundaries