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We present MParrotTTS, a unified multilingual, multi-speaker text-to-speech(TTS) synthesis model that can produce high-quality speech. Benefiting from amodularized training paradigm exploiting self-supervised speechrepresentations, MParrotTTS adapts to a new language with minimal superviseddata and generalizes to languages not seen while training the self-supervisedbackbone. Moreover, without training on any bilingual or parallel examples,MParrotTTS can transfer voices across languages while preserving thespeaker-specific characteristics, e.g., synthesizing fluent Hindi speech usinga French speaker's voice and accent. We present extensive results on sixlanguages in terms of speech naturalness and speaker similarity in parallel andcross-lingual synthesis. The proposed model outperforms the state-of-the-artmultilingual TTS models and baselines, using only a small fraction ofsupervised training data. Speech samples from our model can be found athttps://paper2438.github.io/tts/

MParrotTTS: Multilingual Multi-speaker Text to Speech Synthesis in Low Resource Setting