We introduce an approach to multilingual speech synthesis which uses the
meta-learning concept of contextual parameter generation and produces
natural-sounding multilingual speech using more languages and less training
data than previous approaches. Our model is based on Tacotron 2 with a fully
convolutional input text encoder whose weights are predicted by a separate
parameter generator network. To boost voice cloning, the model uses an
adversarial speaker classifier with a gradient reversal layer that removes
speaker-specific information from the encoder.
We arranged two experiments to compare our model with baselines using various
levels of cross-lingual parameter sharing, in order to evaluate: (1) stability
and performance when training on low amounts of data, (2) pronunciation
accuracy and voice quality of code-switching synthesis. For training, we used
the CSS10 dataset and our new small dataset based on Common Voice recordings in
five languages. Our model is shown to effectively share information across
languages and according to a subjective evaluation test, it produces more
natural and accurate code-switching speech than the baselines.

本文介绍一种多语言语音合成方法，该方法使用上下文参数生成的元学习概念，使用更少的训练数据和更多的语言产生自然音质的多语言语音；其模型使用 Tacotron 2 和完全卷积输入文本编码器，在训练中使用 CSS10 数据集和新的基于 5 种语言的通用语音数据集，同时使用对抗分类器和梯度反转层增强语音克隆。实验结果表明，该模型跨语言信息共享效果良好，在 “代码切换” 语音合成中表现更自然、准确。