Speech is known to carry health-related attributes, which has emerged as a
novel venue for remote and long-term health monitoring. However, existing
models are usually tailored for a specific type of disease, and have been shown
to lack generalizability across datasets. Furthermore, concerns have been
raised recently towards the leakage of speaker identity from health embeddings.
To mitigate these limitations, we propose WavRx, a speech health diagnostics
model that captures the respiration and articulation related dynamics from a
universal speech representation. Our in-domain and cross-domain experiments on
six pathological speech datasets demonstrate WavRx as a new state-of-the-art
health diagnostic model. Furthermore, we show that the amount of speaker
identity entailed in the WavRx health embeddings is significantly reduced
without extra guidance during training. An in-depth analysis of the model was
performed, thus providing physiological interpretation of its improved
generalizability and privacy-preserving ability.

提出了一种能够捕捉通用语音表示中呼吸与表达动态的语音健康诊断模型 WavRx，该模型在六个病理语音数据集上的领域内和领域间实验表明 WavRx 是一种新的最先进的健康诊断模型，并且在没有额外指导的情况下，WavRx 健康嵌入中包含的说话者身份信息得到显著降低，同时通过对模型的深入分析提供了其改进的泛化能力和隐私保护能力的生理解释。