QS-TTS: Towards Semi-Supervised Text-to-Speech Synthesis via
  Vector-Quantized Self-Supervised Speech Representation Learning
Haohan Guo Fenglong Xie Jiawen Kang Yujia Xiao Xixin Wu Helen Meng
This paper proposes a novel semi-supervised TTS framework, QS-TTS, to improve
TTS quality with lower supervised data requirements via Vector-Quantized
Self-Supervised Speech Representation Learning (VQ-S3RL) utilizing more
unlabeled speech audio. This framework comprises two VQ-S3R learners: first,
the principal learner aims to provide a generative Multi-Stage Multi-Codebook
(MSMC) VQ-S3R via the MSMC-VQ-GAN combined with the contrastive S3RL, while
decoding it back to the high-quality audio; then, the associate learner further
abstracts the MSMC representation into a highly-compact VQ representation
through a VQ-VAE. These two generative VQ-S3R learners provide profitable
speech representations and pre-trained models for TTS, significantly improving
synthesis quality with the lower requirement for supervised data. QS-TTS is
evaluated comprehensively under various scenarios via subjective and objective
tests in experiments. The results powerfully demonstrate the superior
performance of QS-TTS, winning the highest MOS over supervised or
semi-supervised baseline TTS approaches, especially in low-resource scenarios.
Moreover, comparing various speech representations and transfer learning
methods in TTS further validates the notable improvement of the proposed
VQ-S3RL to TTS, showing the best audio quality and intelligibility metrics. The
trend of slower decay in the synthesis quality of QS-TTS with decreasing
supervised data further highlights its lower requirements for supervised data,
indicating its great potential in low-resource scenarios.