Speaker-Conditioned Phrase Break Prediction for Text-to-Speech with Phoneme-Level Pre-trained Language Model

Dong Yang; Yuki Saito; Takaaki Saeki; Tomoki Koriyama; Wataru Nakata; Detai Xin; Hiroshi Saruwatari

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arXiv · 2509.00675

Speaker-Conditioned Phrase Break Prediction for Text-to-Speech with Phoneme-Level Pre-trained Language Model

Dong Yang·Yuki Saito·Takaaki Saeki·Tomoki Koriyama·Wataru Nakata·Detai Xin·Hiroshi Saruwatari

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Abstract

This paper advances phrase break prediction (also known as phrasing) in multi-speaker text-to-speech (TTS) systems. We integrate speaker-specific features by leveraging speaker embeddings to enhance the performance of the phrasing model. We further demonstrate that these speaker embeddings can capture speaker-related characteristics solely from the phrasing task. Besides, we explore the potential of pre-trained speaker embeddings for unseen speakers through a few-shot adaptation method. Furthermore, we pioneer the application of phoneme-level pre-trained language models to this TTS front-end task, which significantly boosts the accuracy of the phrasing model. Our methods are rigorously assessed through both objective and subjective evaluations, demonstrating their effectiveness.

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BibTeX RIS

Dong Yang, Yuki Saito, Takaaki Saeki, Tomoki Koriyama, Wataru Nakata, Detai Xin, Hiroshi Saruwatari. 2025-08-31. Speaker-Conditioned Phrase Break Prediction for Text-to-Speech with Phoneme-Level Pre-trained Language Model. https://arxiv.org/abs/2509.00675

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