Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GraphSpeech: Syntax-Aware Graph Attention Network For Neural Speech
  Synthesis   Attention-based end-to-end text-to-speech synthesis (TTS) is superior to
conventional statistical methods in many ways. Transformer-based TTS is one of
such successful implementations. While Transformer TTS models the speech frame
sequence well with a self-attention mechanism, it does not associate input text
with output utterances from a syntactic point of view at sentence level. We
propose a novel neural TTS model, denoted as GraphSpeech, that is formulated
under graph neural network framework. GraphSpeech encodes explicitly the
syntactic relation of input lexical tokens in a sentence, and incorporates such
information to derive syntactically motivated character embeddings for TTS
attention mechanism. Experiments show that GraphSpeech consistently outperforms
the Transformer TTS baseline in terms of spectrum and prosody rendering of
utterances.

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The categories of this paper are: physics
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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: chem-ph
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The categories of this paper are: q-bio
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The categories of this paper are: patt-sol
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The categories of this paper are: plasm-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ph
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The categories of this paper are: nlin
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The categories of this paper are: cmp-lg