TLDR: This research explores using large language models (LLMs) to generate synthetic data for phrase break prediction in text-to-speech systems. It demonstrates that LLM-generated annotations are comparable to human annotations in quality and consistency, significantly reducing manual effort and cost. The study shows this approach is effective across multiple languages and that models trained on synthetic data perform as well as, or better than, those trained on human-annotated data, offering a scalable solution for speech data challenges.
Creating natural-sounding speech for text-to-speech (TTS) systems is a complex task, especially when it comes to placing pauses correctly. These pauses, known as phrase breaks, are crucial for making synthesized speech sound natural and easy to understand. Traditionally, identifying these phrase breaks has relied heavily on human annotators, a process that is both time-consuming and expensive.
There are two main ways human annotators have approached this: audio-oriented and text-oriented. Audio-oriented annotations involve listening to recordings and marking pauses, but this can be inconsistent due to variations in recording quality and speaker styles. Text-oriented annotations, on the other hand, involve linguistic experts analyzing sentence structure to determine pause placements, which requires extensive expertise and significant time.
Both methods face challenges, including the high cost and effort involved in building large, high-quality datasets, especially for multiple languages. This is where the innovative research by Hoyeon Lee, Sejung Son, Ye-Eun Kang, and Jong-Hwan Kim comes into play. Their paper, titled “Synthetic Data Generation for Phrase Break Prediction with Large Language Model,” explores a groundbreaking solution: using large language models (LLMs) to generate synthetic phrase break annotations.
The core idea is to leverage the power of LLMs, which have shown remarkable success in generating tailored synthetic data for various natural language processing (NLP) tasks. The researchers investigated whether LLMs could effectively create high-quality phrase break data, thereby reducing the reliance on manual annotation and addressing the complexities of speech-related data.
The methodology involved using a cutting-edge LLM, GPT-4o mini, and a carefully designed prompt that instructed the LLM to act as a linguistic expert. This prompt guided the LLM to mark phonetic pauses with “#” and sentence boundaries with “/” without altering the original text. To enhance accuracy, the LLM was configured to “read aloud” sentences before annotating, mimicking human annotation processes.
The study conducted an exploratory analysis in English, comparing LLM-generated annotations with both audio-oriented and text-oriented human annotations. A key finding was that while zero-shot prompting (without examples) was ineffective, providing even a few examples significantly improved the LLM’s ability to align with human annotations. Specifically, LLM annotations showed strong alignment with text-oriented human annotations, suggesting their capability to capture structural prosodic patterns from text.
The research also extended its evaluation to French and Spanish, languages with fewer resources compared to English. It was found that incorporating English data, alongside target language data, improved the quality of annotations in these languages. This cross-lingual knowledge transfer demonstrated the generalizability of the approach, although linguistic differences between languages (e.g., stress-timed English vs. syllable-timed Spanish) required careful balancing of example data.
Perhaps the most significant finding was the practical impact of these synthetic annotations on model performance. When a smaller multilingual model (MiniLM) was trained on LLM-generated annotations, it achieved comparable or even superior performance to models trained on human annotations. This indicates that LLM-generated data can feasibly replace human annotations, offering a cost-efficient and consistent solution for phrase break prediction.
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In conclusion, this research highlights the immense potential of LLMs in overcoming data challenges in the speech domain. By generating high-quality, consistent, and cost-efficient synthetic phrase break annotations with minimal examples, LLMs offer a promising path forward for developing more natural and contextually appropriate text-to-speech systems, even for languages with limited resources. For more details, you can read the full research paper here.
