论文标题
使用联邦学习评估抑郁症的隐私敏感语音分析
Privacy Sensitive Speech Analysis Using Federated Learning to Assess Depression
论文作者
论文摘要
最近的研究使用语音信号来评估抑郁症。但是，语音功能可能导致严重的隐私问题。为了解决这些问题，先前的工作使用了保护隐私的语音功能。但是，使用一部分功能可以导致信息丢失，从而导致非最佳模型性能。此外，先前的工作依靠一种集中式方法来支持连续模型更新，从而带来了隐私风险。本文提议使用联邦学习（FL）来实现分散的，保护隐私的语音分析以评估抑郁症。使用现有的数据集（DAIC-WOZ），我们表明，与集中式方法相比，FL模型可以对抑郁症进行强大的抑郁评估，准确性损失仅为4--6％。这些模型还使用相同的数据集优于先前的工作。此外，FL模型的推理潜伏期短，并且记忆力较小，同时具有节能。因此，可以将这些模型部署在移动设备上，以进行实时，连续和隐私的抑郁评估。
Recent studies have used speech signals to assess depression. However, speech features can lead to serious privacy concerns. To address these concerns, prior work has used privacy-preserving speech features. However, using a subset of features can lead to information loss and, consequently, non-optimal model performance. Furthermore, prior work relies on a centralized approach to support continuous model updates, posing privacy risks. This paper proposes to use Federated Learning (FL) to enable decentralized, privacy-preserving speech analysis to assess depression. Using an existing dataset (DAIC-WOZ), we show that FL models enable a robust assessment of depression with only 4--6% accuracy loss compared to a centralized approach. These models also outperform prior work using the same dataset. Furthermore, the FL models have short inference latency and small memory footprints while being energy-efficient. These models, thus, can be deployed on mobile devices for real-time, continuous, and privacy-preserving depression assessment at scale.
