A Discourse-level Multi-scale Prosodic Model for Fine-grained Emotion
  Analysis
Xianhao Wei Jia Jia Xiang Li Zhiyong Wu Ziyi Wang
This paper explores predicting suitable prosodic features for fine-grained
emotion analysis from the discourse-level text. To obtain fine-grained
emotional prosodic features as predictive values for our model, we extract a
phoneme-level Local Prosody Embedding sequence (LPEs) and a Global Style
Embedding as prosodic speech features from the speech with the help of a style
transfer model. We propose a Discourse-level Multi-scale text Prosodic Model
(D-MPM) that exploits multi-scale text to predict these two prosodic features.
The proposed model can be used to analyze better emotional prosodic features
and thus guide the speech synthesis model to synthesize more expressive speech.
To quantitatively evaluate the proposed model, we contribute a new and
large-scale Discourse-level Chinese Audiobook (DCA) dataset with more than
13,000 utterances annotated sequences to evaluate the proposed model.
Experimental results on the DCA dataset show that the multi-scale text
information effectively helps to predict prosodic features, and the
discourse-level text improves both the overall coherence and the user
experience. More interestingly, although we aim at the synthesis effect of the
style transfer model, the synthesized speech by the proposed text prosodic
analysis model is even better than the style transfer from the original speech
in some user evaluation indicators.