# An investigation of phrase break prediction in an End-to-End TTS system

arXiv: https://arxiv.org/abs/2304.04157 · HTML: https://mlsanity.com/paper/2304.04157/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 48 references that are in the map.
- First posted on arXiv 9 Apr 2023.

## Builds on (most cited first, 14 of 14)

- [Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers](https://mlsanity.com/paper/2301.02111/) (2023)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [Efficient Neural Audio Synthesis](https://mlsanity.com/paper/1802.08435/) (2018)
- [Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision](https://mlsanity.com/paper/2302.03540/) (2023)
- [Hierarchical Generative Modeling for Controllable Speech Synthesis](https://mlsanity.com/paper/1810.07217/) (2018)
- [Learning latent representations for style control and transfer in end-to-end speech synthesis](https://mlsanity.com/paper/1812.04342/) (2018)
- [Robust and fine-grained prosody control of end-to-end speech synthesis](https://mlsanity.com/paper/1811.02122/) (2018)
- [Location-Relative Attention Mechanisms For Robust Long-Form Speech Synthesis](https://mlsanity.com/paper/1910.10288/) (2019)
- [Mellotron: Multispeaker expressive voice synthesis by conditioning on rhythm, pitch and global style tokens](https://mlsanity.com/paper/1910.11997/) (2019)
- [Effective Use of Variational Embedding Capacity in Expressive End-to-End Speech Synthesis](https://mlsanity.com/paper/1906.03402/) (2019)
- [Phrase break prediction with bidirectional encoder representations in Japanese text-to-speech synthesis](https://mlsanity.com/paper/2104.12395/) (2021)
- [Unsupervised Style and Content Separation by Minimizing Mutual Information for Speech Synthesis](https://mlsanity.com/paper/2003.06227/) (2020)
- [Using VAEs and Normalizing Flows for One-shot Text-To-Speech Synthesis of Expressive Speech](https://mlsanity.com/paper/1911.12760/) (2019)
- [Modeling Prosodic Phrasing with Multi-Task Learning in Tacotron-based TTS](https://mlsanity.com/paper/2008.05284/) (2020)

## Built on by (most cited first, 2 of 2)

- [Emotion-Aware Prosodic Phrasing for Expressive Text-to-Speech](https://mlsanity.com/paper/2309.11724/) (2023)
- [Improving French Synthetic Speech Quality via SSML Prosody Control](https://mlsanity.com/paper/2508.17494/) (2025)

## Abstract (arXiv)

Purpose: This work explores the use of external phrase break prediction models to enhance listener comprehension in End-to-End Text-to-Speech (TTS) systems. Methods: The effectiveness of these models is evaluated based on listener preferences in subjective tests. Two approaches are explored: (1) a bidirectional LSTM model with task-specific embeddings trained from scratch, and (2) a pre-trained BERT model fine-tuned on phrase break prediction. Both models are trained on a multi-speaker English corpus to predict phrase break locations in text. The End-to-End TTS system used comprises a Tacotron2 model with Dynamic Convolutional Attention for mel spectrogram prediction and a WaveRNN vocoder for waveform generation. Results: The listening tests show a clear preference for text synthesized with predicted phrase breaks over text synthesized without them. Conclusion: These results confirm the value of incorporating external phrasing models within End-to-End TTS to enhance listener comprehension.