# Synthetic Data Generation for Phrase Break Prediction with Large Language Model

arXiv: https://arxiv.org/abs/2507.18044 · HTML: https://mlsanity.com/paper/2507.18044/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 17 of its 32 references that are in the map.
- First posted on arXiv 24 Jul 2025.

## Placed in

- Synthetic data and self-improvement for LLMs / [LLM synthetic data generation](https://mlsanity.com/synth/llm-synthetic-data-generation/)

## Builds on (most cited first, 17 of 17)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Is GPT-3 a Good Data Annotator?](https://mlsanity.com/paper/2212.10450/) (2022)
- [Want To Reduce Labeling Cost? GPT-3 Can Help](https://mlsanity.com/paper/2108.13487/) (2021)
- [Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations](https://mlsanity.com/paper/2310.07849/) (2023)
- [Beyond correlation: The Impact of Human Uncertainty in Measuring the Effectiveness of Automatic Evaluation and LLM-as-a-Judge](https://mlsanity.com/paper/2410.03775/) (2024)
- [The Same But Different: Structural Similarities and Differences in Multilingual Language Modeling](https://mlsanity.com/paper/2410.09223/) (2024)
- [Quality Matters: Evaluating Synthetic Data for Tool-Using LLMs](https://mlsanity.com/paper/2409.16341/) (2024)
- [Phrase break prediction with bidirectional encoder representations in Japanese text-to-speech synthesis](https://mlsanity.com/paper/2104.12395/) (2021)
- [Large Language Models are Good Multi-lingual Learners : When LLMs Meet Cross-lingual Prompts](https://mlsanity.com/paper/2409.11056/) (2024)
- [AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models](https://mlsanity.com/paper/2406.16714/) (2024)
- [Multi-stage Large Language Model Pipelines Can Outperform GPT-4o in Relevance Assessment](https://mlsanity.com/paper/2501.14296/) (2025)
- [Ask Language Model to Clean Your Noisy Translation Data](https://mlsanity.com/paper/2310.13469/) (2023)
- [Cross-Lingual Transfer Learning for Phrase Break Prediction with Multilingual Language Model](https://mlsanity.com/paper/2306.02579/) (2023)

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

- [TriageSim: A Conversational Emergency Triage Simulation Framework from Structured Electronic Health Records](https://mlsanity.com/paper/2603.10035/) (2026)
- [LLM-Based Multi-Reference Evaluation for Efficient and Robust Assessment of Phrase Break Annotations](https://mlsanity.com/paper/2606.21098/) (2026)

## Abstract (arXiv)

Current approaches to phrase break prediction address crucial prosodic aspects of text-to-speech systems but heavily rely on vast human annotations from audio or text, incurring significant manual effort and cost. Inherent variability in the speech domain, driven by phonetic factors, further complicates acquiring consistent, high-quality data. Recently, large language models (LLMs) have shown success in addressing data challenges in NLP by generating tailored synthetic data while reducing manual annotation needs. Motivated by this, we explore leveraging LLM to generate synthetic phrase break annotations, addressing the challenges of both manual annotation and speech-related tasks by comparing with traditional annotations and assessing effectiveness across multiple languages. Our findings suggest that LLM-based synthetic data generation effectively mitigates data challenges in phrase break prediction and highlights the potential of LLMs as a viable solution for the speech domain.