Abstract
Large-scale pre-trained language models have been shown to be helpful in improving the naturalness of text-to-speech (TTS) models by enabling them to produce more naturalistic prosodic patterns. However, these models are usually word-level or sup-phoneme-level and jointly trained with phonemes, making them inefficient for the downstream TTS task where only phonemes are needed. In this work, we propose a phoneme-level BERT (PL-BERT) with a pretext task of predicting the corresponding graphemes along with the regular masked phoneme predictions. Subjective evaluations show that our phoneme-level BERT encoder has significantly improved the mean opinion scores (MOS) of rated naturalness of synthesized speech compared with the state-of-the-art (SOTA) StyleTTS baseline on out-of-distribution (OOD) texts.
Abstract (translated)
大规模的预训练语言模型已经被证明有助于改善文本到语音(TTS)模型的自然度,因为这些模型能够产生更加自然istic的音段模式。然而,这些模型通常是基于单词或超单词音素级别训练的,并与音素一起训练,因此对于只需要音素的下游TTS任务来说它们效率较低。在这项工作中,我们提出了音素级别的BERT(PL-BERT),并提出了一个 pretext 任务,即预测相应的字符和常规掩盖音素预测的对应字符。主观评估表明,我们的音素级别的BERT编码器显著提高了合成语音的主观评价 scores (MOS) 与 OOD 文本中最先进的风格TTS基准线的 MOS 相比。
URL
https://arxiv.org/abs/2301.08810
