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.

The new essential tremor (ET) classification defined ET-plus (ET-p) as an ET subgroup with additional neurological signs besides action tremor. While deep brain stimulation (DBS) is effective in ET, there are no studies specifically addressing DBS effects in ET-p. 44 patients with medication-refractory ET and thalamic/subthalamic DBS implanted at our center were postoperatively classified into ET and ET-p according to preoperative documentation. Tremor suppression with DBS (stimulation ON vs. preoperative baseline and vs. stimulation OFF), measured via the Fahn–Tolosa–Marin tremor rating scale (TRS), stimulation parameters, and the location of active contacts were compared between patients classified as ET and ET-p. TRS scores at baseline were higher in ET-p. ET-p patients showed comparable tremor reduction as patients with ET, albeit higher stimulation parameters were needed in ET-p. Active electrode contacts were located more dorsally in ET-p of uncertain reason. Our data show that DBS is similarly effective in ET-p compared to ET. TRS scores were higher in ET-p preoperatively, and higher stimulation parameters were needed for tremor reduction compared to ET. The latter may be related to a more dorsal location of active electrode contacts in the ET-p group of this cohort. Prospective studies are warranted to investigate DBS in ET-p further.

The present study investigates speech changes in Parkinson’s disease on the acoustic and articulatory level with respect to prosodic prominence marking. To display movements of the underlying articulators, speech data from 16 patients with Parkinson’s disease were recorded using electromagnetic articulography. Speech tasks focused on strategies of prominence marking. Patients’ ability to encode prominence in the laryngeal and supra-laryngeal domain is tested in two conditions to examine the influence of motor performance on speech production further: without dopaminergic medication and with dopaminergic medication. The data reveal that patients with Parkinson’s disease are able to highlight important information in both conditions. They maintain prominence relations across- and within-accentuation by adjusting prosodic markers, such as vowel duration and pitch modulation, while the acoustic vowel space remains the same. For differentiating across-accentuation, not only intensity but also all temporal and spatial parameters related to the articulatory tongue body movements during the production of vowels are modulated to signal prominence. In response to the levodopa intake, gross motor performance improved significantly by 42%. The improvement in gross motor performance was accompanied by an improvement in speech motor performance in terms of louder speech and shorter, larger and faster tongue body movements. The tongue body is more agile under levodopa increase, a fact that is not necessarily detectable on the acoustic level but important for speech therapy.

It is still under debate whether levodopa treatment improves speech functions in Parkinson’s disease (PD). Therefore, speech functions of people with PD were compared in medication-OFF condition (withdrawal of PD medication for at least 12 h) and medication-ON condition (after receiving 200 mg of soluble levodopa). A total of 78 participants, including 51 males and 27 females, performed predefined standard speech tasks. Acoustic speech features were automatically extracted with the algorithm given by the Dysarthria Analyzer. Results suggest that acute levodopa intake improves phonatory-respiratory speech functions and speech planning abilities, while the articulatory system remains unaffected. Furthermore, the study provided preliminary evidence that speech function is able to predict the medication status in individuals with PD as the constructed speech-based biomarker score did not only correlate with established measures of (speech) motor impairment but could also differentiate between the medication OFF and ON status. A post-hoc machine learning model yielded similar results.