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Multiple sclerosis (MS) is a chronic neuroinflammatory disease characterized by central nervous system demyelination and axonal degeneration. Fatigue affects a major portion of MS patients, significantly impairing their daily activities and quality of life. Despite its prevalence, the mechanisms underlying fatigue in MS are poorly understood, and measuring fatigue remains a challenging task. This study evaluates the efficacy of automated speech analysis in detecting fatigue in MS patients. MS patients underwent a detailed clinical assessment and performed a comprehensive speech protocol. Using features from three different free speech tasks and a proprietary cognition score, our support vector machine model achieved an AUC on the ROC of 0.74 in detecting fatigue. Using only free speech features evoked from a picture description task we obtained an AUC of 0.68. This indicates that specific free speech patterns can be useful in detecting fatigue. Moreover, cognitive fatigue was significantly associated with lower speech ratio in free speech (ρ = −0.283, p = 0.001), suggesting that it may represent a specific marker of fatigue in MS patients. Together, our results show that automated speech analysis, of a single narrative free speech task, offers an objective, ecologically valid and low-burden method for fatigue assessment. Speech analysis tools offer promising potential applications in clinical practice for improving disease monitoring and management.

IntroductionDysarthria, 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.MethodsThe 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.ResultsVerification 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.DiscussionThe 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.

Introduction: Progressive cognitive decline is the cardinal behavioral symptom in most dementia-causing diseases such as Alzheimer’s disease. While most well-established measures for cognition might not fit tomorrow’s decentralized remote clinical trials, digital cognitive assessments will gain importance. We present the evaluation of a novel digital speech biomarker for cognition (SB-C) following the Digital Medicine Society’s V3 framework: verification, analytical validation, and clinical validation. Methods: Evaluation was done in two independent clinical samples: the Dutch DeepSpA (N = 69 subjective cognitive impairment [SCI], N = 52 mild cognitive impairment [MCI], and N = 13 dementia) and the Scottish SPeAk datasets (N = 25, healthy controls). For validation, two anchor scores were used: the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating (CDR) scale. Results: Verification: The SB-C could be reliably extracted for both languages using an automatic speech processing pipeline. Analytical Validation: In both languages, the SB-C was strongly correlated with MMSE scores. Clinical Validation: The SB-C significantly differed between clinical groups (including MCI and dementia), was strongly correlated with the CDR, and could track the clinically meaningful decline. Conclusion: Our results suggest that the ki:e SB-C is an objective, scalable, and reliable indicator of cognitive decline, fit for purpose as a remote assessment in clinical early dementia trials.