Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Explore Long-Range Context feature for Speaker Verification   Capturing long-range dependency and modeling long temporal contexts is proven
to benefit speaker verification tasks. In this paper, we propose the
combination of the Hierarchical-Split block(HS-block) and the Depthwise
Separable Self-Attention(DSSA) module to capture richer multi-range context
speaker features from a local and global perspective respectively.
Specifically, the HS-block splits the feature map and filters into several
groups and stacks them in one block, which enlarges the receptive fields(RFs)
locally. The DSSA module improves the multi-head self-attention mechanism by
the depthwise-separable strategy and explicit sparse attention strategy to
model the pairwise relations globally and captures effective long-range
dependencies in each channel. Experiments are conducted on the Voxceleb and
SITW. Our best system achieves 1.27% EER on the Voxceleb1 test set and 1.56% on
SITW by applying the combination of HS-block and DSSA module.

18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: stat
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The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
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The categories of this paper are: patt-sol
2
The categories of this paper are: math
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The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat