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arXiv · 2606.19792

Exploring Pre-training Benefits on Phoneme Addition through Fine-tuning in Speech Synthesis

Masato Murata·Koichi Miyazaki·Tomoki Koriyama·Tomoki Toda
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Abstract

Transfer learning is widely used for low-resource text-to-speech. When the target corpus contains phonemes unseen in pre-training, the model must expand its phoneme inventory during fine-tuning; we call the process "phoneme addition." However, it remains unclear whether the pre-trained ability to generate seen phonemes contributes to this process. This study investigates phoneme addition in two settings: (1) a simulation setup using LLM-generated phoneme-controlled corpora that enables investigation without considering confounding factors, and (2) a real-speech cross-lingual transfer setup (English to Japanese) to validate whether the findings hold in practice. Experiments in both settings showed that while fine-tuning achieved higher naturalness than training from scratch, it required as much or more data to achieve comparable PER for new phonemes. These results indicate that pre-training mainly contributes to naturalness improvement, but offers limited benefit for phoneme addition.

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Masato Murata, Koichi Miyazaki, Tomoki Koriyama, Tomoki Toda. 2026-06-18. Exploring Pre-training Benefits on Phoneme Addition through Fine-tuning in Speech Synthesis. https://arxiv.org/abs/2606.19792

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