  Collecting high-quality studio recordings of audio is challenging, which
limits the language coverage of text-to-speech (TTS) systems. This paper
proposes a framework for scaling a multilingual TTS model to 100+ languages
using found data without supervision. The proposed framework combines
speech-text encoder pretraining with unsupervised training using untranscribed
speech and unspoken text data sources, thereby leveraging massively
multilingual joint speech and text representation learning. Without any
transcribed speech in a new language, this TTS model can generate intelligible
speech in >30 unseen languages (CER difference of <10% to ground truth). With
just 15 minutes of transcribed, found data, we can reduce the intelligibility
difference to 1% or less from the ground-truth, and achieve naturalness scores
that match the ground-truth in several languages.
