Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploiting Syntactic Features in a Parsed Tree to Improve End-to-End TTS   The end-to-end TTS, which can predict speech directly from a given sequence
of graphemes or phonemes, has shown improved performance over the conventional
TTS. However, its predicting capability is still limited by the
acoustic/phonetic coverage of the training data, usually constrained by the
training set size. To further improve the TTS quality in pronunciation, prosody
and perceived naturalness, we propose to exploit the information embedded in a
syntactically parsed tree where the inter-phrase/word information of a sentence
is organized in a multilevel tree structure. Specifically, two key features:
phrase structure and relations between adjacent words are investigated.
Experimental results in subjective listening, measured on three test sets, show
that the proposed approach is effective to improve the pronunciation clarity,
prosody and naturalness of the synthesized speech of the baseline system.

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The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th