Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Universal Neural Vocoding with Parallel WaveNet   We present a universal neural vocoder based on Parallel WaveNet, with an
additional conditioning network called Audio Encoder. Our universal vocoder
offers real-time high-quality speech synthesis on a wide range of use cases. We
tested it on 43 internal speakers of diverse age and gender, speaking 20
languages in 17 unique styles, of which 7 voices and 5 styles were not exposed
during training. We show that the proposed universal vocoder significantly
outperforms speaker-dependent vocoders overall. We also show that the proposed
vocoder outperforms several existing neural vocoder architectures in terms of
naturalness and universality. These findings are consistent when we further
test on more than 300 open-source voices.

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The categories of this paper are: gr-qc
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The categories of this paper are: atom-ph
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The categories of this paper are: stat
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The categories of this paper are: nucl-th
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The categories of this paper are: math
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The categories of this paper are: hep-lat
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The categories of this paper are: ao-sci
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The categories of this paper are: comp-gas
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The categories of this paper are: nlin
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The categories of this paper are: chao-dyn
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The categories of this paper are: econ
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The categories of this paper are: patt-sol
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The categories of this paper are: physics
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The categories of this paper are: hep-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: funct-an