  Most previous neural text-to-speech (TTS) methods are mainly based on
supervised learning methods, which means they depend on a large training
dataset and hard to achieve comparable performance under low-resource
conditions. To address this issue, we propose a semi-supervised learning method
for neural TTS in which labeled target data is limited, which can also resolve
the problem of exposure bias in the previous auto-regressive models.
Specifically, we pre-train the reference model based on Fastspeech2 with much
source data, fine-tuned on a limited target dataset. Meanwhile, pseudo labels
generated by the original reference model are used to guide the fine-tuned
model's training further, achieve a regularization effect, and reduce the
overfitting of the fine-tuned model during training on the limited target data.
Experimental results show that our proposed semi-supervised learning scheme
with limited target data significantly improves the voice quality for test data
to achieve naturalness and robustness in speech synthesis.
