Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training Speaker Embedding Extractors Using Multi-Speaker Audio with
  Unknown Speaker Boundaries   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.

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The categories of this paper are: astro-ph
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
1
The categories of this paper are: cs
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg