  This paper integrates graph-to-sequence into an end-to-end text-to-speech
framework for syntax-aware modelling with syntactic information of input text.
Specifically, the input text is parsed by a dependency parsing module to form a
syntactic graph. The syntactic graph is then encoded by a graph encoder to
extract the syntactic hidden information, which is concatenated with phoneme
embedding and input to the alignment and flow-based decoding modules to
generate the raw audio waveform. The model is experimented on two languages,
English and Mandarin, using single-speaker, few samples of target speakers, and
multi-speaker datasets, respectively. Experimental results show better prosodic
consistency performance between input text and generated audio, and also get
higher scores in the subjective prosodic evaluation, and show the ability of
voice conversion. Besides, the efficiency of the model is largely boosted
through the design of the AI chip operator with 5x acceleration.
