Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease, often affecting speech due to bulbar dysfunction. In this study, we predict speech impairment in people with ALS (pwALS) using two clinical speech-related scores. We evaluate cross-sectional (across speakers) and personalised (within-speaker) modelling paradigms and analyse the utility of common speech tasks to contribute to the standardisation of speech data collection for pwALS. Experiments on a German-speaking cohort of 66 pwALS show that repetition tasks (/da/-/da/, /da/-/ba/) achieved the best cross-sectional performance (Concordance Correlation Coefficient (CCC) = 0.62) for predicting the Quality of Life in the Dysarthric Speaker questionnaire, while the within-speaker setting reached a CCC of 0.86. This study represents an initial step towards speech impairment prediction in German-speaking pwALS and highlights the potential of automated speech analysis as a supportive tool for speech impairment assessment.

翻译：肌萎缩侧索硬化症（ALS）是一种神经退行性疾病，常因球部功能障碍影响言语功能。本研究利用两种临床言语相关评分，对ALS患者（pwALS）的言语障碍进行预测。我们评估了跨被试（speaker）与个体化（within-speaker）两种建模范式，并分析了常见言语任务在规范化pwALS言语数据采集中的实用价值。针对66名德语语系pwALS的实验表明，在预测构音障碍者生活质量问卷（QoL-DyS）评分时，重复任务（/da/-/da/、/da/-/ba/）在跨被试设置中取得最佳性能（一致性相关系数CCC=0.62），而个体化设置下的CCC达到0.86。本研究是德语语系pwALS言语障碍预测的初步探索，揭示了自动化言语分析作为言语障碍评估辅助工具的潜力。