# GraphSpeech: Syntax-Aware Graph Attention Network For Neural Speech Synthesis

arXiv: https://arxiv.org/abs/2010.12423 · HTML: https://mlsanity.com/paper/2010.12423/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [Transformers in Speech Processing: A Survey](https://mlsanity.com/paper/2303.11607/) (2023)
- [Review of end-to-end speech synthesis technology based on deep learning](https://mlsanity.com/paper/2104.09995/) (2021)
- [Explicit Intensity Control for Accented Text-to-speech](https://mlsanity.com/paper/2210.15364/) (2022)
- [Enhancing Word-Level Semantic Representation via Dependency Structure for Expressive Text-to-Speech Synthesis](https://mlsanity.com/paper/2104.06835/) (2021)
- [Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo Conversion](https://mlsanity.com/paper/2305.16353/) (2023)
- [RWEN-TTS: Relation-aware Word Encoding Network for Natural Text-to-Speech Synthesis](https://mlsanity.com/paper/2212.07939/) (2022)
- [Leveraging the Interplay Between Syntactic and Acoustic Cues for Optimizing Korean TTS Pause Formation](https://mlsanity.com/paper/2404.02592/) (2024)
- [Morpho-VITS: Variational Inference with Morphological Modeling for End-to-End Speech Synthesis of a Tonal Bantu Language](https://mlsanity.com/paper/2609.24310/) (2026)

## Abstract (arXiv)

Attention-based end-to-end text-to-speech synthesis (TTS) is superior to conventional statistical methods in many ways. Transformer-based TTS is one of such successful implementations. While Transformer TTS models the speech frame sequence well with a self-attention mechanism, it does not associate input text with output utterances from a syntactic point of view at sentence level. We propose a novel neural TTS model, denoted as GraphSpeech, that is formulated under graph neural network framework. GraphSpeech encodes explicitly the syntactic relation of input lexical tokens in a sentence, and incorporates such information to derive syntactically motivated character embeddings for TTS attention mechanism. Experiments show that GraphSpeech consistently outperforms the Transformer TTS baseline in terms of spectrum and prosody rendering of utterances.