韵律驱动的隐私保护痴呆检测
Prosody-Driven Privacy-Preserving Dementia Detection
AI总结：
本文提出一种基于领域知识的韵律特征解耦方法，在不依赖痴呆分类器的情况下对说话人嵌入进行匿名化，在ADReSS和ADReSSo数据集上同时实现高痴呆检测性能和强说话人隐私保护。
摘要：
从语音录音中提取的说话人嵌入已被证明对痴呆检测具有价值。然而，就其本质而言，这些嵌入包含可识别信息，从而引发隐私担忧。在本工作中，我们旨在对嵌入进行匿名化处理，同时保留其用于痴呆检测的诊断效用。以往研究依赖对抗学习以及在目标属性上训练的模型，并且在资源受限的环境中表现不佳。我们提出一种新方法，利用领域知识从说话人嵌入中解耦与痴呆相关的韵律特征，而无需依赖痴呆分类器。我们的实验表明，该方法在保护说话人隐私方面有效（说话人识别F1分数为0.01%），同时在ADReSS数据集上保持74%的高痴呆检测F1分数。我们的结果也与更具约束性的依赖分类器的系统在ADReSSo上相当（0.01%和0.66%），并且对合成语音的自然度没有影响。
英文摘要：
Speaker embeddings extracted from voice recordings have been proven valuable for dementia detection. However, by their nature, these embeddings contain identifiable information which raises privacy concerns. In this work, we aim to anonymize embeddings while preserving the diagnostic utility for dementia detection. Previous studies rely on adversarial learning and models trained on the target attribute and struggle in limited-resource settings. We propose a novel approach that leverages domain knowledge to disentangle prosody features relevant to dementia from speaker embeddings without relying on a dementia classifier. Our experiments show the effectiveness of our approach in preserving speaker privacy (speaker recognition F1-score .01%) while maintaining high dementia detection score F1-score of 74% on the ADReSS dataset. Our results are also on par with a more constrained classifier-dependent system on ADReSSo (.01% and .66%), and have no impact on synthesized speech naturalness.
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。
