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：现有大多数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。本文提出一种新颖的半监督文本到语音合成模型，通过同时利用配对数据与非配对数据来解决该问题。该模型的核心组件是集成在序列自编码器中的动态量化表示模块。在输入配对数据时，该模块通过可训练码本学习基于配对数据监督的量化表示。然而受限于低资源场景下配对数据的匮乏，这些配对数据难以覆盖所有音素。为此，训练过程中引入非配对数据以扩展动态码本，通过添加与现有量化表示向量距离足够远的新向量。实验表明，在配对数据不足120分钟的条件下，本文方法在主客观指标上均优于现有方法。