While neural methods for text-to-speech (TTS) have shown great advances in
modeling multiple speakers, even in zero-shot settings, the amount of data
needed for those approaches is generally not feasible for the vast majority of
the world's over 6,000 spoken languages. In this work, we bring together the
tasks of zero-shot voice cloning and multilingual low-resource TTS. Using the
language agnostic meta learning (LAML) procedure and modifications to a TTS
encoder, we show that it is possible for a system to learn speaking a new
language using just 5 minutes of training data while retaining the ability to
infer the voice of even unseen speakers in the newly learned language. We show
the success of our proposed approach in terms of intelligibility, naturalness
and similarity to target speaker using objective metrics as well as human
studies and provide our code and trained models open source.

在这项工作中，我们将零样本语音克隆和多语言低资源语音合成的任务结合在一起。通过使用语言不可知的元学习（LAML）程序和对 TTS 编码器的修改，我们证明了一个系统可以学习在只有 5 分钟的训练数据下说一个新语言，同时保留了推断新学习语言中甚至看不到的说话者声音的能力，并提供了开源的代码和训练模型。