Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Weakly Supervised PLDA Training   PLDA is a popular normalization approach for the i-vector model, and it has
delivered state-of-the-art performance in speaker verification. However, PLDA
training requires a large amount of labelled development data, which is highly
expensive in most cases. We present a cheap PLDA training approach, which
assumes that speakers in the same session can be easily separated, and speakers
in different sessions are simply different. This results in `weak labels' which
are not fully accurate but cheap, leading to a weak PLDA training.
  Our experimental results on real-life large-scale telephony customer service
achieves demonstrated that the weak training can offer good performance when
human-labelled data are limited. More interestingly, the weak training can be
employed as a discriminative adaptation approach, which is more efficient than
the prevailing unsupervised method when human-labelled data are insufficient.

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The categories of this paper are: adap-org
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The categories of this paper are: eess
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The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
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The categories of this paper are: econ
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci
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The categories of this paper are: atom-ph
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The categories of this paper are: funct-an
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The categories of this paper are: cmp-lg
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The categories of this paper are: nlin
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The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th