Paper ID: 2406.08911

An Initial Investigation of Language Adaptation for TTS Systems under Low-resource Scenarios

Cheng Gong, Erica Cooper, Xin Wang, Chunyu Qiang, Mengzhe Geng, Dan Wells, Longbiao Wang, Jianwu Dang, Marc Tessier, Aidan Pine, Korin Richmond, Junichi Yamagishi

Self-supervised learning (SSL) representations from massively multilingual models offer a promising solution for low-resource language speech tasks. Despite advancements, language adaptation in TTS systems remains an open problem. This paper explores the language adaptation capability of ZMM-TTS, a recent SSL-based multilingual TTS system proposed in our previous work. We conducted experiments on 12 languages using limited data with various fine-tuning configurations. We demonstrate that the similarity in phonetics between the pre-training and target languages, as well as the language category, affects the target language's adaptation performance. Additionally, we find that the fine-tuning dataset size and number of speakers influence adaptability. Surprisingly, we also observed that using paired data for fine-tuning is not always optimal compared to audio-only data. Beyond speech intelligibility, our analysis covers speaker similarity, language identification, and predicted MOS.

Submitted: Jun 13, 2024