Towards Speech Impairment Prediction in German-Speaking Individuals with Amyotrophic Lateral Sclerosis
For clinicians and researchers, this provides an initial step towards automated speech analysis for ALS assessment in German speakers, but the small cohort and moderate cross-sectional performance limit its immediate impact.
This study predicts speech impairment in German-speaking individuals with ALS using clinical speech scores, achieving a cross-sectional CCC of 0.62 and a within-speaker CCC of 0.86 for the Quality of Life in the Dysarthric Speaker questionnaire.
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.