  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.
