Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: UniTTS: Residual Learning of Unified Embedding Space for Speech Style
  Control   We propose a novel high-fidelity expressive speech synthesis model, UniTTS,
that learns and controls overlapping style attributes avoiding interference.
UniTTS represents multiple style attributes in a single unified embedding space
by the residuals between the phoneme embeddings before and after applying the
attributes. The proposed method is especially effective in controlling multiple
attributes that are difficult to separate cleanly, such as speaker ID and
emotion, because it minimizes redundancy when adding variance in speaker ID and
emotion, and additionally, predicts duration, pitch, and energy based on the
speaker ID and emotion. In experiments, the visualization results exhibit that
the proposed methods learned multiple attributes harmoniously in a manner that
can be easily separated again. As well, UniTTS synthesized high-fidelity speech
signals controlling multiple style attributes. The synthesized speech samples
are presented at
https://anonymous-authors2022.github.io/paper_works/UniTTS/demos/.

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The categories of this paper are: solv-int
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The categories of this paper are: q-fin
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The categories of this paper are: alg-geom
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The categories of this paper are: funct-an
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The categories of this paper are: hep-lat
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The categories of this paper are: nlin
2
The categories of this paper are: math
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The categories of this paper are: comp-gas
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The categories of this paper are: supr-con
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The categories of this paper are: adap-org
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The categories of this paper are: q-bio
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga
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The categories of this paper are: physics
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The categories of this paper are: ao-sci