Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Impact of Channel Variation on One-Class Learning for Spoof Detection   The value of Spoofing detection in increasing the reliability of the ASV
system is unparalleled. In reality, however, the performance of countermeasure
systems (CMs) degrades significantly due to channel variation.
Multi-conditional training(MCT) is a well-established technique to handle such
scenarios. However, "which data-feeding strategy is optimal for MCT?" is not
known in the case of spoof detection. In this paper, various codec simulations
were used to modify ASVspoof 2019 dataset, and assessments were done using
data-feeding and mini-batching strategies to help address this question. Our
experiments aim to test the efficacy of the various margin-based losses for
training Resnet based models with LFCC front-end feature extractor to correctly
classify the spoofed and bonafide samples degraded using codec simulations.
Contrastingly to most of the works that focus mainly on architectures, this
study highlights the relevance of the deemed-of-low-importance process of
data-feeding and mini-batching to raise awareness of the need to refine it for
better performance.

15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph