Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploring Voice Conversion based Data Augmentation in Text-Dependent
  Speaker Verification   In this paper, we focus on improving the performance of the text-dependent
speaker verification system in the scenario of limited training data. The
speaker verification system deep learning based text-dependent generally needs
a large scale text-dependent training data set which could be labor and cost
expensive, especially for customized new wake-up words. In recent studies,
voice conversion systems that can generate high quality synthesized speech of
seen and unseen speakers have been proposed. Inspired by those works, we adopt
two different voice conversion methods as well as the very simple re-sampling
approach to generate new text-dependent speech samples for data augmentation
purposes. Experimental results show that the proposed method significantly
improves the Equal Error Rare performance from 6.51% to 4.51% in the scenario
of limited training data.

20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: alg-geom