  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.
