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.

翻译：大多数现有基于神经网络的文本到语音合成方法依赖大规模数据集，在低资源条件下面临挑战。本文提出一种新型半监督文本到语音合成模型，通过利用成对与非成对数据解决该问题。该模型的关键组件是一个动态量化表示模块，它被集成到序列自编码器中。当输入成对数据时，该模块通过可训练码本在成对数据监督下学习量化表示。然而，低资源场景中有限的成对数据难以覆盖所有音素。为此，在训练过程中引入非成对数据，通过添加与现有量化表示向量距离足够远的新向量来扩展动态码本。实验表明，在成对数据不足120分钟的条件下，本方法在主客观指标上均优于现有方法。