State-of-the-art text-to-speech (TTS) systems have utilized pretrained language models (PLMs) to enhance prosody and create more natural-sounding speech. However, while PLMs have been extensively researched for natural language understanding (NLU), their impact on TTS has been overlooked. In this study, we aim to address this gap by conducting a comparative analysis of different PLMs for two TTS tasks: prosody prediction and pause prediction. Firstly, we trained a prosody prediction model using 15 different PLMs. Our findings revealed a logarithmic relationship between model size and quality, as well as significant performance differences between neutral and expressive prosody. Secondly, we employed PLMs for pause prediction and found that the task was less sensitive to small models. We also identified a strong correlation between our empirical results and the GLUE scores obtained for these language models. To the best of our knowledge, this is the first study of its kind to investigate the impact of different PLMs on TTS.

翻译：当前最先进的文本转语音（TTS）系统已利用预训练语言模型（PLM）来增强韵律并生成更自然的语音。然而，尽管PLM在自然语言理解（NLU）领域得到了广泛研究，其对TTS的影响却长期被忽视。本研究旨在填补这一空白，针对两项TTS任务——韵律预测与停顿预测，对不同PLM进行了比较分析。首先，我们采用15种不同的PLM训练了一个韵律预测模型。研究结果揭示了模型规模与质量之间的对数关系，并发现中性韵律与表现性韵律之间存在显著性能差异。其次，我们将PLM应用于停顿预测，发现该任务对小型模型的敏感度较低。此外，我们观察到实证结果与这些语言模型在GLUE得分之间存在强相关性。据我们所知，这是首项系统探究不同PLM对TTS影响的研究。