Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Speech Enhancement-assisted Stargan Voice Conversion in Noisy
  Environments   Numerous voice conversion (VC) techniques have been proposed for the
conversion of voices among different speakers. Although the decent quality of
converted speech can be observed when VC is applied in a clean environment, the
quality will drop sharply when the system is running under noisy conditions. In
order to address this issue, we propose a novel enhancement-based StarGAN
(E-StarGAN) VC system, which leverages a speech enhancement (SE) technique for
signal pre-processing. SE systems are generally used to reduce noise components
in noisy speech and to generate enhanced speech for downstream application
tasks. Therefore, we investigated the effectiveness of E-StarGAN, which
combines VC and SE, and demonstrated the robustness of the proposed approach in
various noisy environments. The results of VC experiments conducted on a
Mandarin dataset show that when combined with SE, the proposed E-StarGAN VC
model is robust to unseen noises. In addition, the subjective listening test
results show that the proposed E-StarGAN model can improve the sound quality of
speech signals converted from noise-corrupted source utterances.

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The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
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The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
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The categories of this paper are: econ