This paper aims to enhance low-resource TTS by reducing training data
requirements using compact speech representations. A Multi-Stage Multi-Codebook
(MSMC) VQ-GAN is trained to learn the representation, MSMCR, and decode it to
waveforms. Subsequently, we train the multi-stage predictor to predict MSMCRs
from the text for TTS synthesis. Moreover, we optimize the training strategy by
leveraging more audio to learn MSMCRs better for low-resource languages. It
selects audio from other languages using speaker similarity metric to augment
the training set, and applies transfer learning to improve training quality. In
MOS tests, the proposed system significantly outperforms FastSpeech and VITS in
standard and low-resource scenarios, showing lower data requirements. The
proposed training strategy effectively enhances MSMCRs on waveform
reconstruction. It improves TTS performance further, which wins 77% votes in
the preference test for the low-resource TTS with only 15 minutes of paired
data.

本文提出了一种用于提高低资源的 TTS 系统性能的方法，其利用紧凑的语音表示并利用 Multi-Stage Multi-Codebook (MSMC) VQ-GAN 学习 MSMCR 表示并解码成波形，并使用多阶段预测器从文本中预测 MSMCRs 进行 TTS 合成，并通过优化训练策略，利用训练集增强了微型语言环境下的训练质量，并在 MOS 测试中显示出优异的性能。