Understanding the transferability of language and gender-based phenotypic expression of specific acoustic measures is essential for applying digital speech biomarkers in potential future clinical trials. This study aimed to identify possible gender- or language-related differences in speech between men and women with multiple system atrophy (MSA). A total of 42 male and 40 female MSA patients, along with 41 male and 41 female age-matched healthy controls, were recruited from two centres representing two distinct languages: Czech and Italian. A quantitative acoustic assessment was performed using 12 distinct speech dimensions. No significant clinical differences in MSA patients were found between men and women in terms of age, disease duration, motor severity, or dysarthria severity. MSA patients exhibited significantly worse performance compared to controls for voice quality, pitch breaks, frequency and amplitude vocal tremor, slow and irregular sequential motion rates, imprecise consonants, dynamics of articulation, monopitch, excessive loudness variation, articulation rate, and inappropriate silences (p < 0.001). Considering the gender-specific patterns, the female MSA patients manifested more impaired voice quality (p < 0.05) and more frequent vocal tremor (p < 0.05) then male MSA, while male MSA patients showed slower diadochokinetic rate (p < 0.01) and higher excessive loudness variability (p < 0.01) than female MSA. The impact of language on disease-related changes appears to be minimal for the majority of acoustic parameters considered. Despite some gender differences, our findings demonstrate that speech-based digital biomarkers in MSA offer high discriminatory power while maintaining good consistency across gender and language.

• Keywords
• Acoustic analyses., Dysarthria, Gender, Multiple system atrophy, Sex,
• Publication type
• Journal Article MeSH

BACKGROUND AND OBJECTIVES: Patients with synucleinopathies such as multiple system atrophy (MSA) and Parkinson's disease (PD) frequently display speech and language abnormalities. We explore the diagnostic potential of automated linguistic analysis of natural spontaneous speech to differentiate MSA and PD. METHODS: Spontaneous speech of 39 participants with MSA compared to 39 drug-naive PD and 39 healthy controls matched for age and sex was transcribed and linguistically annotated using automatic speech recognition and natural language processing. A quantitative analysis was performed using 6 lexical and syntactic and 2 acoustic features. Results were compared with human-controlled analysis to assess the robustness of the approach. Diagnostic accuracy was evaluated using sensitivity analysis. RESULTS: Despite similar disease duration, linguistic abnormalities were generally more severe in MSA than in PD, leading to high diagnostic accuracy with an area under the curve of 0.81. Compared to controls, MSA showed decreased grammatical component usage, more repetitive phrases, shorter sentences, reduced sentence development, slower articulation rate, and increased duration of pauses, whereas PD had only shorter sentences, reduced sentence development, and longer pauses. Only slower articulation rate was distinctive for MSA while unchanged for PD relative to controls. The highest correlation was found between bulbar/pseudobulbar clinical score and sentence length (r = -0.49, p = 0.002). Despite the relatively high severity of dysarthria in MSA, a strong agreement between manually and automatically computed results was achieved. DISCUSSION: Automated linguistic analysis may offer an objective, cost-effective, and widely applicable biomarker to differentiate synucleinopathies with similar clinical manifestations.

• Keywords
• Automated linguistic analysis, Language, Multiple system atrophy, Natural language processing, Spontaneous discourse,
• MeSH
• Diagnosis, Differential MeSH
• Middle Aged MeSH
• Humans MeSH
• Multiple System Atrophy * diagnosis   complications   MeSH
• Parkinson Disease * diagnosis   complications   MeSH
• Aged MeSH
• Natural Language Processing * MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

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.

• Keywords
• Huntington's disease (HD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), digital biomarkers, intelligibility, progressive supranuclear palsy (PSP), speech analysis,
• Publication type
• Journal Article MeSH

While speech disorder represents an early and prominent clinical feature of atypical parkinsonian syndromes such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), little is known about the sensitivity of speech assessment as a potential diagnostic tool. Speech samples were acquired from 215 subjects, including 25 MSA, 20 PSP, 20 Parkinson's disease participants, and 150 healthy controls. The accurate differential diagnosis of dysarthria subtypes was based on the quantitative acoustic analysis of 26 speech dimensions related to phonation, articulation, prosody, and timing. A semi-supervised weighting-based approach was then applied to find the best feature combinations for separation between PSP and MSA. Dysarthria was perceptible in all PSP and MSA patients and consisted of a combination of hypokinetic, spastic, and ataxic components. Speech features related to respiratory dysfunction, imprecise consonants, monopitch, slow speaking rate, and subharmonics contributed to worse performance in PSP than MSA, whereas phonatory instability, timing abnormalities, and articulatory decay were more distinctive for MSA compared to PSP. The combination of distinct speech patterns via objective acoustic evaluation was able to discriminate between PSP and MSA with very high accuracy of up to 89% as well as between PSP/MSA and PD with up to 87%. Dysarthria severity in MSA/PSP was related to overall disease severity. Speech disorders reflect the differing underlying pathophysiology of tauopathy in PSP and α-synucleinopathy in MSA. Vocal assessment may provide a low-cost alternative screening method to existing subjective clinical assessment and imaging diagnostic approaches.

• Publication type
• Journal Article MeSH

Hypokinetic dysarthria is a multidimensional impairment affecting all main speech subsystems with variable patterns and severity across individual Parkinson's disease (PD) patients. We can thus assume that inter-individual abnormal speech patterns are related to the various clinical subtypes of PD with different prominent motor symptoms. The aim of this cross-sectional study was to compare speech disorder between patients with the postural instability/gait difficulty (PIGD) and tremor-dominant (TD) motor phenotypes of PD. Speech samples were acquired from a total of 63 participants, including 21 PIGD patients, 21 TD patients, and 21 healthy controls. Quantitative acoustic vocal assessment of 12 unique speech dimensions related to phonation, vocal tremor, oral diadochokinesis, articulation, prosody and speech timing was performed. Speech impairment was more pronounced in the PIGD group than in the TD group, with an area under the curve of 0.76. Patients in the PIGD group manifested abnormalities in pitch breaks, articulatory decay, decreased rate of follow-up speech segments and inappropriate silences, apart from monopitch and irregular AMR that were affected in TD group as well. An abnormal vocal tremor was present in only 10% of PD patients, with no differences between the PD phenotypes. We found a correlation between non-motor symptom severity and speech timing (r = - 0.40, p = 0.009). The present study demonstrates that speech disorder reflects the underlying motor phenotypes. Vocal tremor appeared to be an isolated phenomenon that does not share similar pathophysiology with limb tremor.

• Keywords
• Acoustic analyses, Dysarthria, Gait, Parkinson’s disease, Phenotype, Speech disorder, Vocal tremor,
• MeSH
• Gait MeSH
• Humans MeSH
• Gait Disorders, Neurologic * MeSH
• Parkinson Disease * complications   MeSH
• Voice Disorders * etiology   MeSH
• Postural Balance MeSH
• Cross-Sectional Studies MeSH
• Speech MeSH
• Tremor complications   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH