论文标题
语言敏锐的元学习，用于低资源的文本到语音，具有关节功能
Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features
论文作者
论文摘要
尽管神经文本到语音系统在高资源场景中的表现非常出色，但由于缺乏适当的培训数据，它们不能应用于世界上6,000多种语言中的大多数。在这项工作中，我们使用源自发音矢量的嵌入，而不是来自音素身份的嵌入来学习跨语言的音素表示。结合语言不可知论的元学习，这使我们能够以先前看不见的说话者说的以前看不见的语言来微调高质量的文本到语音模型。
While neural text-to-speech systems perform remarkably well in high-resource scenarios, they cannot be applied to the majority of the over 6,000 spoken languages in the world due to a lack of appropriate training data. In this work, we use embeddings derived from articulatory vectors rather than embeddings derived from phoneme identities to learn phoneme representations that hold across languages. In conjunction with language agnostic meta learning, this enables us to fine-tune a high-quality text-to-speech model on just 30 minutes of data in a previously unseen language spoken by a previously unseen speaker.
