Navigating the challenges of data-driven speech processing, one of the primary hurdles is accessing reliable pathological speech data. While public datasets appear to offer solutions, they come with inherent risks of potential unintended exposure of patient health information via re-identification attacks. Using a comprehensive real-world pathological speech corpus, with over n=3,800 test subjects spanning various age groups and speech disorders, we employed a deep-learning-driven automatic speaker verification (ASV) approach. This resulted in a notable mean equal error rate (EER) of 0.89% with a standard deviation of 0.06%, outstripping traditional benchmarks. Our comprehensive assessments demonstrate that pathological speech overall faces heightened privacy breach risks compared to healthy speech. Specifically, adults with dysphonia are at heightened re-identification risks, whereas conditions like dysarthria yield results comparable to those of healthy speakers. Crucially, speech intelligibility does not influence the ASV system's performance metrics. In pediatric cases, particularly those with cleft lip and palate, the recording environment plays a decisive role in re-identification. Merging data across pathological types led to a marked EER decrease, suggesting the potential benefits of pathological diversity in ASV, accompanied by a logarithmic boost in ASV effectiveness. In essence, this research sheds light on the dynamics between pathological speech and speaker verification, emphasizing its crucial role in safeguarding patient confidentiality in our increasingly digitized healthcare era.

翻译：在数据驱动语音处理的挑战中，获取可靠的病理语音数据是主要障碍之一。虽然公开数据集看似提供了解决方案，但它们存在固有风险，即通过重识别攻击可能导致患者健康信息的意外泄露。利用一个包含超过n=3,800名测试对象、涵盖各年龄段及多种言语障碍的真实世界病理语音语料库，我们采用深度学习驱动的自动说话人验证（ASV）方法。结果取得了显著的平均等错误率（EER）0.89%，标准差为0.06%，超越了传统基准。我们的综合评估表明，与健康语音相比，病理语音整体面临更高的隐私泄露风险。具体而言，患有发声困难的成年人面临更高的重识别风险，而构音障碍等病症的结果则与健康说话人相当。关键的是，语音清晰度并不影响ASV系统的性能指标。在儿科病例中，尤其是唇腭裂患儿，录音环境对重识别起决定性作用。跨病理类型的数据融合导致EER显著下降，表明ASV中病理多样性具有潜在益处，伴随ASV效果的对数提升。本质上，本研究揭示了病理语音与说话人验证之间的动态关系，强调了在日益数字化的医疗时代保护患者机密的关键作用。