mlsanityData to 14 Oct 2026

Synthetic Data Generation for Phrase Break Prediction with Large Language Model

arXiv 2507.18044 ↗First posted 24 Jul 2025cs.CL cs.AIcited by 2

On the map: Synthetic data and self-improvement for LLMs → LLM synthetic data generation

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.

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.

Builds on

17 of 32 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Quality Matters: Evaluating Synthetic Data for Tool-Using LLMs24 Sep
  2. 2023Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations11 Oct

Other references · 15

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2020MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformerscited by 1,463
  4. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,407
  5. 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,384
  6. 2022Is GPT-3 a Good Data Annotator?cited by 208
  7. 2021Want To Reduce Labeling Cost? GPT-3 Can Helpcited by 196
  8. 2024Beyond correlation: The Impact of Human Uncertainty in Measuring the Effectiveness of Automatic Evaluation and LLM-as-a-Judgecited by 31
  9. 2024The Same But Different: Structural Similarities and Differences in Multilingual Language Modelingcited by 28
  10. 2021Phrase break prediction with bidirectional encoder representations in Japanese text-to-speech synthesiscited by 12
  11. 2024Large Language Models are Good Multi-lingual Learners : When LLMs Meet Cross-lingual Promptscited by 10
  12. 2024AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Modelscited by 9
3 more
  1. 2025Multi-stage Large Language Model Pipelines Can Outperform GPT-4o in Relevance Assessmentcited by 7
  2. 2023Ask Language Model to Clean Your Noisy Translation Datacited by 3
  3. 2023Cross-Lingual Transfer Learning for Phrase Break Prediction with Multilingual Language Modelcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026TriageSim: A Conversational Emergency Triage Simulation Framework from Structured Electronic Health Recordscited by 2
  2. 2026LLM-Based Multi-Reference Evaluation for Efficient and Robust Assessment of Phrase Break Annotationscited by 0

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  4. 2025LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context
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  7. 2024Paralinguistics-Aware Speech-Empowered Large Language Models for Natural Conversation
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