# Exploiting Syntactic Features in a Parsed Tree to Improve End-to-End TTS

arXiv: https://arxiv.org/abs/1904.04764 · HTML: https://mlsanity.com/paper/1904.04764/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2019.

## Built on by (most cited first, 10 of 10)

- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [Review of end-to-end speech synthesis technology based on deep learning](https://mlsanity.com/paper/2104.09995/) (2021)
- [ParaTTS: Learning Linguistic and Prosodic Cross-sentence Information in Paragraph-based TTS](https://mlsanity.com/paper/2209.06484/) (2022)
- [Enhancing Word-Level Semantic Representation via Dependency Structure for Expressive Text-to-Speech Synthesis](https://mlsanity.com/paper/2104.06835/) (2021)
- [A Study of Modeling Rising Intonation in Cantonese Neural Speech Synthesis](https://mlsanity.com/paper/2208.02189/) (2022)
- [Cascading and Direct Approaches to Unsupervised Constituency Parsing on Spoken Sentences](https://mlsanity.com/paper/2303.08809/) (2023)
- [Audio-Visual Neural Syntax Acquisition](https://mlsanity.com/paper/2310.07654/) (2023)
- [Applying Syntaxunicodex2013Prosody Mapping Hypothesis and Prosodic Well-Formedness Constraints to Neural Sequence-to-Sequence Speech Synthesis](https://mlsanity.com/paper/2203.15276/) (2022)
- [Using Deepfake Technologies for Word Emphasis Detection](https://mlsanity.com/paper/2305.07791/) (2023)
- [Leveraging the Interplay Between Syntactic and Acoustic Cues for Optimizing Korean TTS Pause Formation](https://mlsanity.com/paper/2404.02592/) (2024)

## Abstract (arXiv)

The end-to-end TTS, which can predict speech directly from a given sequence of graphemes or phonemes, has shown improved performance over the conventional TTS. However, its predicting capability is still limited by the acoustic/phonetic coverage of the training data, usually constrained by the training set size. To further improve the TTS quality in pronunciation, prosody and perceived naturalness, we propose to exploit the information embedded in a syntactically parsed tree where the inter-phrase/word information of a sentence is organized in a multilevel tree structure. Specifically, two key features: phrase structure and relations between adjacent words are investigated. Experimental results in subjective listening, measured on three test sets, show that the proposed approach is effective to improve the pronunciation clarity, prosody and naturalness of the synthesized speech of the baseline system.