Automating dysarthria assessments offers the opportunity to develop effective, low-cost tools that address the current limitations of manual and subjective assessments. Nonetheless, it is unclear whether current approaches rely on dysarthria-related speech patterns or external factors. We aim toward obtaining a clearer understanding of dysarthria patterns. To this extent, we study the effects of noise in recordings, both through addition and reduction. We design and implement a new method for visualizing and comparing feature extractors and models, at a patient level, in a more interpretable way. We use the UA-Speech dataset with a speaker-based split of the dataset. Results reported in the literature appear to have been done irrespective of such split, leading to models that may be overconfident due to data-leakage. We hope that these results raise awareness in the research community regarding the requirements for establishing reliable automatic dysarthria assessment systems.

翻译：自动化构音障碍评估为开发有效、低成本的工具提供了机会，以应对当前人工和主观评估的局限性。然而，目前尚不清楚现有方法是否依赖于与构音障碍相关的语音模式或外部因素。我们旨在更清晰地理解构音障碍模式。为此，我们研究了录音中噪声的影响，包括添加和减少噪声。我们设计并实现了一种新方法，用于在患者层面以更可解释的方式可视化和比较特征提取器与模型。我们使用UA-Speech数据集，并基于说话者进行数据划分。文献中报告的结果似乎未考虑此类划分，导致模型可能因数据泄露而过度自信。我们希望这些结果能引起研究界对建立可靠自动构音障碍评估系统所需条件的重视。