Extending Multilingual Speech Synthesis to 100+ Languages without Transcribed Data
Takaaki Saeki, Gary Wang, Nobuyuki Morioka, Isaac Elias, Kyle Kastner, Andrew Rosenberg, Bhuvana Ramabhadran, Heiga Zen
Abstract
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.
BibTeX
@inproceedings{icassp2024_extendingmultili,
title = {Extending Multilingual Speech Synthesis to 100+ Languages without Transcribed Data},
author = {Takaaki Saeki and Gary Wang and Nobuyuki Morioka and Isaac Elias and Kyle Kastner and Andrew Rosenberg and Bhuvana Ramabhadran and Heiga Zen and Françoise Beaufays and Hadar Shemtov},
booktitle = {ICASSP 2024},
year = {2024}
}