Dependency Parsing based Semantic Representation Learning with Graph Neural Network for Enhancing Expressiveness of Text-to-Speech

By Yixuan Zhou, Jingbei Li, Zhiyong Wu, Helen Mei-Ling Meng. 2021

6 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xu Tan
2. Tao Qin
3. Tie-Yan Liu
4. Frank K. Soong
5. Yi Ren
6. Olivier Chapelle
7. Yanqing Liu
8. Rongjie Huang

Citations in the corpus

• A Survey on Neural Speech Synthesis (2021, background): “• Text pre-training [78, 101, 387, 140, 95, 447], which can provide better text representations by using pre-trained word embeddings or model parameters.”
• SyntaSpeech: Syntax-Aware Generative Adversarial Text-to-Speech (2022, unknown): “Recently, RGGN [ Zhou et al. , 2021 ] also adopts dependency parsing to construct the syntactic graph and utilize pre-trained word embedding from BERT [ Devlin et al. , 2019 ] , then process the graphical data with gated graph neural network (GGNN) [ Li et al. , 2016 ] .”