RESUMEN
Introduction: Dysarthria, a motor speech disorder caused by muscle weakness or paralysis, severely impacts speech intelligibility and quality of life. The condition is prevalent in motor speech disorders such as Parkinson's disease (PD), atypical parkinsonism such as progressive supranuclear palsy (PSP), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS). Improving intelligibility is not only an outcome that matters to patients but can also play a critical role as an endpoint in clinical research and drug development. This study validates a digital measure for speech intelligibility, the ki: SB-M intelligibility score, across various motor speech disorders and languages following the Digital Medicine Society (DiMe) V3 framework. Methods: The study used four datasets: healthy controls (HCs) and patients with PD, HD, PSP, and ALS from Czech, Colombian, and German populations. Participants' speech intelligibility was assessed using the ki: SB-M intelligibility score, which is derived from automatic speech recognition (ASR) systems. Verification with inter-ASR reliability and temporal consistency, analytical validation with correlations to gold standard clinical dysarthria scores in each disease, and clinical validation with group comparisons between HCs and patients were performed. Results: Verification showed good to excellent inter-rater reliability between ASR systems and fair to good consistency. Analytical validation revealed significant correlations between the SB-M intelligibility score and established clinical measures for speech impairments across all patient groups and languages. Clinical validation demonstrated significant differences in intelligibility scores between pathological groups and healthy controls, indicating the measure's discriminative capability. Discussion: The ki: SB-M intelligibility score is a reliable, valid, and clinically relevant tool for assessing speech intelligibility in motor speech disorders. It holds promise for improving clinical trials through automated, objective, and scalable assessments. Future studies should explore its utility in monitoring disease progression and therapeutic efficacy as well as add data from further dysarthrias to the validation.
RESUMEN
The Rey Auditory Verbal Learning Test (RAVLT) is an established verbal learning test commonly used to quantify memory impairments due to Alzheimer's Disease (AD) both at a clinical dementia stage or prodromal stage of mild cognitive impairment (MCI). Focal memory impairment-as quantified e.g. by the RAVLT-at an MCI stage is referred to as amnestic MCI (aMCI) and is often regarded as the cognitive phenotype of prodromal AD. However, recent findings suggest that not only learning and memory but also other cognitive domains, especially executive functions (EF) and processing speed (PS), influence verbal learning performance. This research investigates whether additional temporal features extracted from audio recordings from a participant's RAVLT response can better dissociate memory and EF in such tasks and eventually help to better describe MCI subtypes. 675 age-matched participants from the H70 Swedish birth cohort were included in this analysis; 68 participants were classified as MCI (33 aMCI and 35 due to executive impairment). RAVLT performances were recorded and temporal features extracted. Novel temporal features were correlated with established neuropsychological tests measuring EF and PS. Lastly, the downstream diagnostic potential of temporal features was estimated using group differences and a machine learning (ML) classification scenario. Temporal features correlated moderately with measures of EF and PS. Performance of an ML classifier could be improved by adding temporal features to traditional counts. We conclude that RAVLT temporal features are in general related to EF and that they might be capable of dissociating memory and EF in a word list learning task.