For text-to-speech (TTS) synthesis, prosodic structure prediction (PSP) plays
an important role in producing natural and intelligible speech. Although
inter-utterance linguistic information can influence the speech interpretation
of the target utterance, previous works on PSP mainly focus on utilizing
intrautterance linguistic information of the current utterance only. This work
proposes to use inter-utterance linguistic information to improve the
performance of PSP. Multi-level contextual information, which includes both
inter-utterance and intrautterance linguistic information, is extracted by a
hierarchical encoder from character level, utterance level and discourse level
of the input text. Then a multi-task learning (MTL) decoder predicts prosodic
boundaries from multi-level contextual information. Objective evaluation
results on two datasets show that our method achieves better F1 scores in
predicting prosodic word (PW), prosodic phrase (PPH) and intonational phrase
(IPH). It demonstrates the effectiveness of using multi-level contextual
information for PSP. Subjective preference tests also indicate the naturalness
of synthesized speeches are improved.

利用多级语境信息，通过多任务学习解码器，本研究提出了一种改进语音生成自然性的方法，通过使用跨发话语之间的语言信息来预测韵律边界，从而在两个数据集上实现了更好的预测效果。

多层次上下文信息提升普通话韵律结构预测

Improving Mandarin Prosodic Structure Prediction with Multi-level  Contextual Information

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.

本文提出了一种基于 BERT 和自注意力结构实现的基于跨度的汉语韵律结构预测模型，通过对所提供的语言学特征进行编码得到最优韵律结构树，再利用自下而上的 CKY 算法找到得分最高的韵律树，从而完成汉字转韵律标签的端到端预测，实验结果显示：该方法比所有基于序列到序列的基线方法都要优异。