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Large-scale pre-trained language models have been shown to be helpful inimproving the naturalness of text-to-speech (TTS) models by enabling them toproduce more naturalistic prosodic patterns. However, these models are usuallyword-level or sup-phoneme-level and jointly trained with phonemes, making theminefficient for the downstream TTS task where only phonemes are needed. In thiswork, we propose a phoneme-level BERT (PL-BERT) with a pretext task ofpredicting the corresponding graphemes along with the regular masked phonemepredictions. Subjective evaluations show that our phoneme-level BERT encoderhas significantly improved the mean opinion scores (MOS) of rated naturalnessof synthesized speech compared with the state-of-the-art (SOTA) StyleTTSbaseline on out-of-distribution (OOD) texts.

Phoneme-Level BERT for Enhanced Prosody of Text-to-Speech with Grapheme Predictions