标题： SecureSpeech：基于提示的说话人和内容保护 显示英文标题

标题： SecureSpeech: Prompt-based Speaker and Content Protection

摘要： 鉴于身份盗窃和通过语音内容重新识别说话者带来的日益严重的隐私问题，本文提出了一种基于提示的语音生成流程，以确保说话者身份和语音内容的双重匿名化。 这是通过1）生成与源说话者不可关联的说话者身份，由描述符控制，以及2）使用名称实体识别模型和大型语言模型替换原始文本中的敏感内容来实现的。 该流程利用匿名化的说话者身份和文本通过文本到语音合成模型生成高质量、隐私友好的语音。 实验结果表明，在保持一定的内容保留和音频质量水平的同时，实现了显著的隐私保护。 本文还研究了不同说话者描述对生成语音的实用性和隐私的影响，以确定潜在的偏见。 显示英文摘要

摘要： Given the increasing privacy concerns from identity theft and the re-identification of speakers through content in the speech field, this paper proposes a prompt-based speech generation pipeline that ensures dual anonymization of both speaker identity and spoken content. This is addressed through 1) generating a speaker identity unlinkable to the source speaker, controlled by descriptors, and 2) replacing sensitive content within the original text using a name entity recognition model and a large language model. The pipeline utilizes the anonymized speaker identity and text to generate high-fidelity, privacy-friendly speech via a text-to-speech synthesis model. Experimental results demonstrate an achievement of significant privacy protection while maintaining a decent level of content retention and audio quality. This paper also investigates the impact of varying speaker descriptions on the utility and privacy of generated speech to determine potential biases.