  Most existing neural-based text-to-speech methods rely on extensive datasets
and face challenges under low-resource condition. In this paper, we introduce a
novel semi-supervised text-to-speech synthesis model that learns from both
paired and unpaired data to address this challenge. The key component of the
proposed model is a dynamic quantized representation module, which is
integrated into a sequential autoencoder. When given paired data, the module
incorporates a trainable codebook that learns quantized representations under
the supervision of the paired data. However, due to the limited paired data in
low-resource scenario, these paired data are difficult to cover all phonemes.
Then unpaired data is fed to expand the dynamic codebook by adding quantized
representation vectors that are sufficiently distant from the existing ones
during training. Experiments show that with less than 120 minutes of paired
data, the proposed method outperforms existing methods in both subjective and
objective metrics.
