Reliably evaluating the severity of a speech pathology is crucial in healthcare. However, the current reliance on expert evaluations by speech-language pathologists presents several challenges: while their assessments are highly skilled, they are also subjective, time-consuming, and costly, which can limit the reproducibility of clinical studies and place a strain on healthcare resources. While automated methods exist, they have significant drawbacks. Reference-based approaches require transcriptions or healthy speech samples, restricting them to read speech and limiting their applicability. Existing reference-free methods are also flawed; supervised models often learn spurious shortcuts from data, while handcrafted features are often unreliable and restricted to specific speech tasks. This paper introduces XPPG-PCA (x-vector phonetic posteriorgram principal component analysis), a novel, unsupervised, reference-free method for speech severity evaluation. Using three Dutch oral cancer datasets, we demonstrate that XPPG-PCA performs comparably to, or exceeds established reference-based methods. Our experiments confirm its robustness against data shortcuts and noise, showing its potential for real-world clinical use. Taken together, our results show that XPPG-PCA provides a robust, generalizable solution for the objective assessment of speech pathology, with the potential to significantly improve the efficiency and reliability of clinical evaluations across a range of disorders. An open-source implementation is available.

翻译：可靠评估语音病理的严重程度在医疗保健中至关重要。然而，目前对言语语言病理学家专家评估的依赖存在若干挑战：尽管他们的评估技能高超，但同时也具有主观性、耗时且成本高昂，这可能限制临床研究的可重复性并对医疗资源造成压力。虽然已有自动化方法，但它们存在显著缺陷。基于参考的方法需要转录文本或健康语音样本，这将其限制于朗读语音并降低了适用性。现有的无参考方法也存在不足；监督模型常从数据中学习虚假捷径，而手工设计的特征往往不可靠且仅限于特定语音任务。本文提出XPPG-PCA（x-vector语音后验图主成分分析），一种新颖的无监督、无参考语音严重性评估方法。通过使用三个荷兰口腔癌数据集，我们证明XPPG-PCA的性能与成熟的基于参考方法相当或更优。实验证实了其对数据捷径和噪声的鲁棒性，展现了其在真实临床场景中的应用潜力。综合来看，我们的结果表明XPPG-PCA为语音病理的客观评估提供了鲁棒且可泛化的解决方案，有望显著提升跨多种障碍的临床评估效率与可靠性。开源实现已公开提供。