  Syntactic structure of a sentence text is correlated with the prosodic
structure of the speech that is crucial for improving the prosody and
naturalness of a text-to-speech (TTS) system. Nowadays TTS systems usually try
to incorporate syntactic structure information with manually designed features
based on expert knowledge. In this paper, we propose a syntactic representation
learning method based on syntactic parse tree traversal to automatically
utilize the syntactic structure information. Two constituent label sequences
are linearized through left-first and right-first traversals from constituent
parse tree. Syntactic representations are then extracted at word level from
each constituent label sequence by a corresponding uni-directional gated
recurrent unit (GRU) network. Meanwhile, nuclear-norm maximization loss is
introduced to enhance the discriminability and diversity of the embeddings of
constituent labels. Upsampled syntactic representations and phoneme embeddings
are concatenated to serve as the encoder input of Tacotron2. Experimental
results demonstrate the effectiveness of our proposed approach, with mean
opinion score (MOS) increasing from 3.70 to 3.82 and ABX preference exceeding
by 17% compared with the baseline. In addition, for sentences with multiple
syntactic parse trees, prosodic differences can be clearly perceived from the
synthesized speeches.
