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.

自动化失语症评估可以开发低成本而有效的工具来解决手动和主观评估的当前局限，但目前的方法是否依赖于与失语症相关的说话模式或外部因素尚不清楚。本文研究噪音对记录的失语症的影响，设计并实现了一种新的方法来可视化和比较特征提取器和模型，使用 UA-Speech 数据集，旨在提高研究社区对建立可靠的自动失语症评估系统要求的认识。