Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sequence-To-Sequence Voice Conversion using F0 and Time Conditioning and
  Adversarial Learning   This paper presents a sequence-to-sequence voice conversion (S2S-VC)
algorithm which allows to preserve some aspects of the source speaker during
conversion, typically its prosody, which is useful in many real-life
application of voice conversion. In S2S-VC, the decoder is usually conditioned
on linguistic and speaker embeddings only, with the consequence that only the
linguistic content is actually preserved during conversion. In the proposed
S2S-VC architecture, the decoder is conditioned explicitly on the desired F0
sequence so that the converted speech has the same F0 as the one of the source
speaker, or any F0 defined arbitrarily. Moreover, an adversarial module is
further employed so that the S2S-VC is not only optimized on the available true
speech samples, but can also take efficiently advantage of the converted speech
samples that can be produced by using various conditioning such as speaker
identity, F0, or timing.

5
The categories of this paper are: gr-qc
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The categories of this paper are: econ
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The categories of this paper are: stat
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The categories of this paper are: nucl-ex
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The categories of this paper are: math-ph
2
The categories of this paper are: math
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The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: acc-phys
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The categories of this paper are: cond-mat
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The categories of this paper are: atom-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph