Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Speaker Embedding Learning with Multi-Level Pooling for
  Text-Independent Speaker Verification   This paper aims to improve the widely used deep speaker embedding x-vector
model. We propose the following improvements: (1) a hybrid neural network
structure using both time delay neural network (TDNN) and long short-term
memory neural networks (LSTM) to generate complementary speaker information at
different levels; (2) a multi-level pooling strategy to collect speaker
information from both TDNN and LSTM layers; (3) a regularization scheme on the
speaker embedding extraction layer to make the extracted embeddings suitable
for the following fusion step. The synergy of these improvements are shown on
the NIST SRE 2016 eval test (with a 19% EER reduction) and SRE 2018 dev test
(with a 9% EER reduction), as well as more than 10% DCF scores reduction on
these two test sets over the x-vector baseline.

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The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
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The categories of this paper are: q-fin
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The categories of this paper are: nlin
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The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
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The categories of this paper are: econ
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The categories of this paper are: chao-dyn
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The categories of this paper are: physics
30
The categories of this paper are: q-alg