Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: I-vector Transformation Using Conditional Generative Adversarial
  Networks for Short Utterance Speaker Verification   I-vector based text-independent speaker verification (SV) systems often have
poor performance with short utterances, as the biased phonetic distribution in
a short utterance makes the extracted i-vector unreliable. This paper proposes
an i-vector compensation method using a generative adversarial network (GAN),
where its generator network is trained to generate a compensated i-vector from
a short-utterance i-vector and its discriminator network is trained to
determine whether an i-vector is generated by the generator or the one
extracted from a long utterance. Additionally, we assign two other learning
tasks to the GAN to stabilize its training and to make the generated ivector
more speaker-specific. Speaker verification experiments on the NIST SRE 2008
"10sec-10sec" condition show that our method reduced the equal error rate by
11.3% from the conventional i-vector and PLDA system.

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The categories of this paper are: hep-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
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The categories of this paper are: patt-sol
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The categories of this paper are: gr-qc
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The categories of this paper are: astro-ph
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The categories of this paper are: q-bio
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The categories of this paper are: hep-lat
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-th
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: stat
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The categories of this paper are: econ