Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Investigating Active-learning-based Training Data Selection for Speech
  Spoofing Countermeasure   Training a spoofing countermeasure (CM) that generalizes to various unseen
data is desired but challenging. While methods such as data augmentation and
self-supervised learning are applicable, the imperfect CM performance on
diverse test sets still calls for additional strategies. This study took the
initiative and investigated CM training using active learning (AL), a framework
that iteratively selects useful data from a large pool set and fine-tunes the
CM. This study compared a few methods to measure the data usefulness and the
impact of using different pool sets collected from various sources. The results
showed that the AL-based CMs achieved better generalization than our strong
baseline on multiple test tests. Furthermore, compared with a top-line CM that
simply used the whole data pool set for training, the AL-based CMs achieved
similar performance using less training data. Although no single best
configuration was found for AL, the rule of thumb is to include diverse spoof
and bona fide data in the pool set and to avoid any AL data selection method
that selects the data that the CM feels confident in.

15
The categories of this paper are: nucl-ex
1
The categories of this paper are: cs
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph