# A taxonomic articulation-focused approach to dysarthria classification > **NIH NIH R01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2024 · $546,958 ## Abstract Articulatory impairments are common in talkers with dysarthria and have been shown to contribute most to speech intelligibility loss. However, the treatment of articulatory impairments remains challenging; primarily, because knowledge about articulatory impairment patterns and how they vary across talkers is limited. The Mayo Clinic dysarthria classification system links neurological conditions to specific, auditory-perceptually defined dysarthria types with presumably distinct motor impairment patterns. Based on this framework, prominent textbooks have recommended to specifically target the motor impairments that presumably underlie each dysarthria type. Such an intervention approach, however, still lacks empirical support and is difficult to apply to mixed dysarthria types. Based on the rationale that virtually all talkers with dysarthria exhibit imprecise articulation, impairment-nonspecific behavioral treatments (e.g., loud, clear, slow speech) have gained popularity and are commonly used as therapeutic interventions for talkers with mild to moderate dysarthria regardless of their underlying disease. This rationale, however, ignores the fact that a variety of articulatory behaviors can yield the same auditory-perceptual consequences. Indeed, highly heterogeneous articulatory performance patterns have been reported across and within disease types and motivated us to test the feasibility of a new, taxonomic (data-driven) approach to dysarthria classification based on speech kinematic measures. Our pilot work on a cohort of 28 talkers with Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS) revealed six dysarthria subgroups with unique articulatory impairment profiles addressing temporal and spatial characteristics of vocal tract adjustments as well as labial coupling. Three disease-dominant subgroups and well as three mixed-disease subgroups were identified. This proposed project seeks to expand upon these preliminary findings. In Specific Aim 1, we will classify the articulatory performance profiles of 160 talkers with varying underlying disease types [PD, ALS, MS, Huntington's disease (HD)]. To allow for a clinical interpretation of the kinematic findings, articulatory performance of talkers with dysarthria will be referenced to age- and sex-specific control groups. We will also determine which kinematic measures differentiate talkers with dysarthria and if disease-type varies systematically across articulation- based dysarthria subgroups. To ensure a rapid translation of our kinematic-based classification approach into clinical practice we will determine how perceptual-based clinical ratings of articulatory performance map onto findings of kinematic measures (Specific Aim 2). Raters will judge the articulatory performance of the same 160 talkers with dysarthria using auditory- and visual-perceptual ratings scales. Study outcomes will advance the field's understanding of articulatory impairment patterns ... ## Key facts - **NIH application ID:** 10861828 - **Project number:** 5R01DC019648-03 - **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER - **Principal Investigator:** Antje Mefferd - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $546,958 - **Award type:** 5 - **Project period:** 2022-08-17 → 2027-05-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10861828 ## Citation > US National Institutes of Health, RePORTER application 10861828, A taxonomic articulation-focused approach to dysarthria classification (5R01DC019648-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10861828. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*