Improving Mandarin Prosodic Structure Prediction with Multi-level
  Contextual Information
Jie Chen Changhe Song Deyi Tuo Xixin Wu Shiyin Kang Zhiyong Wu Helen Meng
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.