Different languages have distinct phonetic systems and vary in their prosodic
features making it challenging to develop a Text-to-Speech (TTS) model that can
effectively synthesise speech in multilingual settings. Furthermore, TTS
architecture needs to be both efficient enough to capture nuances in multiple
languages and efficient enough to be practical for deployment. The standard
approach is to build transformer based model such as SpeechT5 and train it on
large multilingual dataset. As the size of these models grow the conventional
fine-tuning for adapting these model becomes impractical due to heavy
computational cost. In this paper, we proposes to integrate parameter-efficient
transfer learning (PETL) methods such as adapters and hypernetwork with TTS
architecture for multilingual speech synthesis. Notably, in our experiments
PETL methods able to achieve comparable or even better performance compared to
full fine-tuning with only $\sim$2.5\% tunable parameters.The code and samples
are available at: this https URL

在多种语言环境中，为了有效地合成语音，开发一个能够应对不同语言的语音合成模型是具有挑战性的。本文提出了一种在多语言语音合成中，将参数高效的迁移学习方法（如适配器和超网络）与 TTS 架构集成的方法，并在实验中证明这种方法在性能上能够与全面微调方法相媲美甚至更好，参数数量仅为总参数的约 2.5%。