预训练语言模型用于文本到语音的对比分析
A Comparative Analysis of Pretrained Language Models for Text-to-Speech
AI总结：
本研究首次对比分析15种预训练语言模型在文本到语音的韵律预测和停顿预测任务中的表现，发现模型大小与质量呈对数关系，且实证结果与GLUE分数强相关。
摘要：
最先进的文本到语音（TTS）系统已利用预训练语言模型（PLMs）来增强韵律并生成更自然的语音。然而，尽管PLMs在自然语言理解（NLU）领域已被广泛研究，其对TTS的影响却被忽视。在本研究中，我们旨在通过针对两个TTS任务（韵律预测和停顿预测）进行不同PLMs的对比分析来填补这一空白。首先，我们使用15种不同的PLMs训练了一个韵律预测模型。我们的发现揭示了模型大小与质量之间的对数关系，以及中性韵律和表达性韵律之间的显著性能差异。其次，我们将PLMs用于停顿预测，发现该任务对小型模型不太敏感。我们还确定了我们的实证结果与这些语言模型获得的GLUE分数之间的强相关性。据我们所知，这是首个调查不同PLMs对TTS影响的研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
