Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworks   We present JHU's system submission to the ASVspoof 2019 Challenge:
Anti-Spoofing with Squeeze-Excitation and Residual neTworks (ASSERT).
Anti-spoofing has gathered more and more attention since the inauguration of
the ASVspoof Challenges, and ASVspoof 2019 dedicates to address attacks from
all three major types: text-to-speech, voice conversion, and replay. Built upon
previous research work on Deep Neural Network (DNN), ASSERT is a pipeline for
DNN-based approach to anti-spoofing. ASSERT has four components: feature
engineering, DNN models, network optimization and system combination, where the
DNN models are variants of squeeze-excitation and residual networks. We
conducted an ablation study of the effectiveness of each component on the
ASVspoof 2019 corpus, and experimental results showed that ASSERT obtained more
than 93% and 17% relative improvements over the baseline systems in the two
sub-challenges in ASVspooof 2019, ranking ASSERT one of the top performing
systems. Code and pretrained models will be made publicly available.

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The categories of this paper are: q-fin
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The categories of this paper are: math
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The categories of this paper are: hep-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: chem-ph
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The categories of this paper are: adap-org
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-alg
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The categories of this paper are: nucl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: mtrl-th
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The categories of this paper are: gr-qc
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The categories of this paper are: q-bio
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The categories of this paper are: stat
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The categories of this paper are: astro-ph
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The categories of this paper are: solv-int