title: Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features
abstract: 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 te
journal: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)