With the advancement of speech synthesis technology, users have higher expectations for the naturalness and expressiveness of synthesized speech. But previous research ignores the importance of prompt selection. This study proposes a text-to-speech (TTS) framework based on Retrieval-Augmented Generation (RAG) technology, which can dynamically adjust the speech style according to the text content to achieve more natural and vivid communication effects. We have constructed a speech style knowledge database containing high-quality speech samples in various contexts and developed a style matching scheme. This scheme uses embeddings, extracted by Llama, PER-LLM-Embedder,and Moka, to match with samples in the knowledge database, selecting the most appropriate speech style for synthesis. Furthermore, our empirical research validates the effectiveness of the proposed method. Our demo can be viewed at: https://thuhcsi.github.io/icme2025-AutoStyle-TTS

本研究针对以往文本到语音合成研究中忽视提示选择的重要性，提出了一种基于检索增强生成(RAG)的TTS框架。该框架能够根据文本内容动态调整语言风格，从而实现更自然生动的交流效果，并通过构建高质量语音样本知识库和风格匹配方案，验证了其有效性。

自动风格TTS：基于检索增强生成的自动风格匹配文本到语音合成