解锁用于隐私增强语音理解的基础模型：基于标签引导合成语音内容的低资源语音训练早期研究
Unlocking Foundation Models for Privacy-Enhancing Speech Understanding: An Early Study on Low Resource Speech Training Leveraging Label-guided Synthetic Speech Content
AI总结：
本研究探索用基础模型助力隐私增强语音计算，提出仅靠标签引导的预训练生成模型合成语音作为训练数据的方法，发现其零样本学习仍有挑战，但合成数据训练的模型可作为低资源ASU训练的有效初始化点，有望减少用户数据收集以增强隐私。
摘要：
自动语音理解（ASU）借助深度学习模型的能力实现对人类语音的精准解读，催生了丰富人类生活的各类语音应用。然而，训练一个鲁棒的ASU模型需要整理大量语音样本，这带来了隐私泄露风险。在本研究中，我们探究利用基础模型助力隐私增强语音计算的方法。与主要聚焦数据扰动或分布式算法的传统研究不同，我们的工作探索了仅通过标签引导、使用预训练生成模型合成语音内容作为训练数据的可能性。我们发现，基于训练标签引导的合成语音内容实现零样本学习仍是一项具有挑战性的任务。另一方面，我们的实验结果表明，用合成语音样本训练的模型可为低资源ASU训练提供有效的初始化点。这一结果揭示了通过减少用户数据收集、采用标签引导合成语音内容来增强隐私的潜力。
英文摘要：
Automatic Speech Understanding (ASU) leverages the power of deep learning models for accurate interpretation of human speech, leading to a wide range of speech applications that enrich the human experience. However, training a robust ASU model requires the curation of a large number of speech samples, creating risks for privacy breaches. In this work, we investigate using foundation models to assist privacy-enhancing speech computing. Unlike conventional works focusing primarily on data perturbation or distributed algorithms, our work studies the possibilities of using pre-trained generative models to synthesize speech content as training data with just label guidance. We show that zero-shot learning with training label-guided synthetic speech content remains a challenging task. On the other hand, our results demonstrate that the model trained with synthetic speech samples provides an effective initialization point for low-resource ASU training. This result reveals the potential to enhance privacy by reducing user data collection but using label-guided synthetic speech content.
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。
