  The accuracy of prosodic structure prediction is crucial to the naturalness
of synthesized speech in Mandarin text-to-speech system, but now is limited by
widely-used sequence-to-sequence framework and error accumulation from previous
word segmentation results. In this paper, we propose a span-based Mandarin
prosodic structure prediction model to obtain an optimal prosodic structure
tree, which can be converted to corresponding prosodic label sequence. Instead
of the prerequisite for word segmentation, rich linguistic features are
provided by Chinese character-level BERT and sent to encoder with
self-attention architecture. On top of this, span representation and label
scoring are used to describe all possible prosodic structure trees, of which
each tree has its corresponding score. To find the optimal tree with the
highest score for a given sentence, a bottom-up CKY-style algorithm is further
used. The proposed method can predict prosodic labels of different levels at
the same time and accomplish the process directly from Chinese characters in an
end-to-end manner. Experiment results on two real-world datasets demonstrate
the excellent performance of our span-based method over all
sequence-to-sequence baseline approaches.
