Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features

• URL: http://arxiv.org/abs/2203.03191v1
• Date: Mon, 7 Mar 2022 07:58:01 GMT
• Title: Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features
• Authors: Florian Lux, Ngoc Thang Vu
• Abstract summary: 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. 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.
• Score: 30.37026279162593
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 text-to-speech model on just 30 minutes of data in a previously unseen language spoken by a previously unseen speaker.

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