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.

本研究旨在比较分析不同的预训练语言模型在文本转语音任务中的影响，包括韵律预测和停顿预测，并发现模型大小与质量之间存在对数关系，以及中性和表达韵律之间的显著性能差异。此外，还发现任务对较小模型不太敏感，并且我们的实证结果与这些语言模型的 GLUE 分数之间存在强相关性。据我们所知，这是第一项研究探究不同预训练语言模型对文本转语音影响的研究。

文本转语音的预训练语言模型的比较分析

A Comparative Analysis of Pretrained Language Models for Text-to-Speech

In this paper, we present ExtremeBERT, a toolkit for accelerating and
customizing BERT pretraining. Our goal is to provide an easy-to-use BERT
pretraining toolkit for the research community and industry. Thus, the
pretraining of popular language models on customized datasets is affordable
with limited resources. Experiments show that, to achieve the same or better
GLUE scores, the time cost of our toolkit is over $6\times$ times less for BERT
Base and $9\times$ times less for BERT Large when compared with the original
BERT paper. The documentation and code are released at
this https URL under the Apache-2.0 license.

本文介绍了 ExtremeBERT，一款用于加速和定制 BERT 预训练的工具包，旨在为研究界和工业界提供易于使用的 BERT 预训练工具包，以便使用有限资源在自定义数据集上训练流行语言模型。实验表明，与原始 BERT 论文相比，使用我们的工具包达到相同或更好的 GLUE 分数的时间成本分别比 BERT Base 和 BERT Large 更低 $6	imes$ 和 $9	imes$ 倍。文档和代码在 Apache-2.0 许可下发布在此 https URL 上。