Cognitive determinants of dysarthria in Parkinson's disease: An automated machine learning approach
Open accessAbstract
Background: Dysarthric symptoms in Parkinson's disease (PD) vary greatly across cohorts. Abundant research suggests that such heterogeneity could reflect subject‐level and task‐related cognitive factors. However, the interplay of these variables during motor speech remains underexplored, let alone by administering validated materials to carefully matched samples with varying cognitive profiles and combining automated tools with machine learning methods. Objective: We aimed to identify which speech dimensions best identify patients with PD in cognitively heterogeneous, cognitively preserved, and cognitively impaired groups through tasks with low (reading) and high (retelling) processing demands. Methods: We used support vector machines to analyze prosodic, articulatory, and phonemic identifiability features. Patient groups were compared with healthy control subjects and against each other in both tasks, using each measure separately and in combination. Results: Relative to control subjects, patients in cognitively heterogeneous and cognitively preserved groups were best discriminated by combined dysarthric signs during reading (accuracy = 84% and 80.2%). Conversely, patients with cognitive impairment were maximally discriminated from control subjects when considering phonemic identifiability during retelling (accuracy = 86.9%). This same pattern maximally distinguished between cognitively spared and impaired patients (accuracy = 72.1%). Also, cognitive (executive) symptom severity was predicted by prosody in cognitively preserved patients and by phonemic identifiability in cognitively heterogeneous and impaired groups. No measure predicted overall motor dysfunction in any group. Conclusions: Predominant dysarthric symptoms appear to be best captured through undemanding tasks in cognitively heterogeneous and preserved cohorts and through cognitively loaded tasks in patients with cognitive impairment. Further applications of this framework could enhance dysarthria assessments in PD. © 2021 International Parkinson and Movement Disorder Society
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Specific acoustic and discourse markers of Alzheimer's disease in English speakers may generalize onto Spanish speakers, and dysarthric aspects that typify Parkinson's disease and its phenotypes seem similar across the languages studied so far.
“Moreover, specific acoustic and discourse markers of Alzheimer's disease in English speakers may generalize onto Spanish speak ers, 88 and dysarthric aspects that typify Parkinson's disease 89 and its phenotypes 20,69,90 seem similar across the languages studied so far.”
From Speech and language markers of neurodegeneration: a call for global equity (2023) · cites this paper as [20]
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Comparative analysis of speech impairment and upper limb motor dysfunction in Parkinson’s disease
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Attention/memory complaint is correlated with motor speech disorder in Parkinson’s disease
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Self-citationOn the Projection of PLLRs for Unbounded Feature Distributions in Spoken Language Recognition
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Fluency adaptation in speakers with Parkinson disease: a motor learning perspective
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Acoustic Characteristics of VOT in Plosive Consonants Produced by Parkinson’s Patients
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Self-citationTime‐Frequency Processing: Spectral Properties
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End-to-end deep learning approach for Parkinson’s disease detection from speech signals
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Self-citationMultivariate word properties in fluency tasks reveal markers of Alzheimer's dementia
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Self-citationModeling and diagnosis Parkinson disease by using hand drawing: deep learning model
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Self-citationExploring digital speech biomarkers of hypokinetic dysarthria in a multilingual cohort
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Self-citationAutomated free speech analysis reveals distinct markers of Alzheimer’s and frontotemporal dementia
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Self-citationToolkit to Examine Lifelike Language v.2.0: Optimizing Speech Biomarkers of Neurodegeneration
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Does cognitive impairment influence motor speech performance in de novo Parkinson's disease?
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Voice Acoustic Instability During Spontaneous Speech in Parkinson’s Disease
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Cognitive Phenotyping of Parkinson's Disease Patients Via Digital Analysis of Spoken Word Properties
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Self-citationArtificial Intelligence‐Powered Acoustic Analysis System for Dysarthria Severity Assessment
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Automated Speech Markers of Alzheimer Dementia: Test of Cross-Linguistic Generalizability
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Self-citationSpeech biomarkers of risk factors for vascular dementia in people with mild cognitive impairment
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Machine Learning for Parkinson’s Disease and Related Disorders
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Electrophysiological alterations during action semantic processing in Parkinson’s disease
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Self-citationAttentional Demand of Motor Speech Encoding: Evidence From Parkinson's Disease
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Self-citationSpeech and language outcome measures in clinical trials of Alzheimer’s and Parkinson’s diseases
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Variability in Vowel Space in Parkinson's Disease: Associations With Cognitive and Motor Impairment
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The phonation test can distinguish the patient with Parkinson’s disease via Bayes inference
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Self-citationAcoustic prosodic parameters associated with Parkinson's disease cognitive impairment
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Self-citationMercado, J., Escobar-Grisales, D., Ríos-Urrego, C. D., García, A. M., Bocanegra, Y., Moreno, L., Nöth, E., & Orozco‐Arroyave, J. R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-02551-7_25 · Cited by 1 · see citing works
Self-citationSpeech Acoustic Markers Can Detect Mild Cognitive Impairment in Parkinson’s Disease
Smith, K. M., Williamson, J. R., & Quatieri, T. F. (2025) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2025.3620716 · Cited by 1 · see citing works
Longitudinal Evaluation of Dysarthria Progression in Patients with Parkinson’s Disease
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Self-citationLan, J., Li, W., Ding, M., Qi, J., & Wang, Y. (2026) · Clinical Neurology and Neurosurgery · doi:10.1016/j.clineuro.2026.109469 · Cited by 1 · see citing works
The include network: Advancing cross‐linguistic equity in brain health research
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Self-citationRecognition of Parkinson’s ailment by using various machine learning procedures
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Automatic Score of Articulatory Distortion in Adults with DysarthriaPreprint
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Self-citationGarcía, A. M. (2023) · European Journal of Neurology · doi:10.1111/ene.15766
Self-citationEscobar-Grisales, D., Ríos-Urrego, C. D., & Orozco‐Arroyave, J. R. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-74595-9_7
Self-citationSpeech Representations to Monitor Parkinson Disease Progression
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Self-citationShankar, R., Goh, Z., & Qian, X. (2025) · medRxiv · doi:10.1101/2025.10.11.25337799
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Escobar-Grisales, D., Ríos-Urrego, C. D., & Orozco‐Arroyave, J. R. (2025) · Springer eBooks · doi:10.1007/978-3-031-95878-6_1
Self-citationImpact of Amazonian Dance on Speech Performance in People with Parkinson’s DiseasePreprint
Prates, R. A. C. S., Guzzo, C. C. E., Duarte, M. V. A., Sánchez, A. M., Pauli, P., Martinez, F. G., Monteiro, E. P., García, A. M., Haas, A. N., & Theofanopoulou, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.12.699086
Self-citationKrithiga, L., & B, F. K. A. (2025) · International Conference on Intelligent Computing, Information and Control Systems (ICOIICS) · doi:10.1109/icoiics67115.2025.11390342
Automated detection of Parkinson's disease using speech signals
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Digital profiling of dysarthria in late- and early-onset Parkinson's disease
Escobar-Grisales, D., Olchik, M. R., Arias‐Vergara, T., dos Santos, V. B., Bressanelli, A. L., Mata, I. F., Orozco‐Arroyave, J. R., & García, A. M. (2026) · Journal of Parkinson s Disease · doi:10.1177/1877718x261467587
Self-citationImpact of Amazonian dance on speech performance in people with Parkinson’s disease
Prates, R. A. C. S., Guzzo, C. C. E., Duarte, M. V. A., Sánchez, A. M., Pauli, P., Martinez, F. G., Monteiro, E. P., García, A. M., Haas, A. N., & Theofanopoulou, C. (2026) · iScience · doi:10.1016/j.isci.2026.117185 · Cited by 1 · see citing works
Self-citationHu, Y., Shen, C., Bai, Y., & Wu, R.-Y. (2026) · doi:10.2196/preprints.111048
- Authors
- García, A. M., Arias‐Vergara, T., C. Vasquez‐Correa, J., Nöth, E., Schuster, M., Welch, A. E., Bocanegra, Y., Baena, A., & Orozco‐Arroyave, J. R.
- Affiliations
- Universidad de Santiago de Chile (CL)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- GITA Lab, Faculty of Engineering Universidad de Antioquia UdeA Medellín Colombia
- Ludwig-Maximilians-Universität München (DE)
- University of California, San Francisco (US)
- Universidad de Antioquia (CO)
- Corresponding author
- García, Adolfo M.
- Orozco‐Arroyave, Juan R.
- ORCID
- Venue
- Movement Disorders
- Publisher
- Wiley
- Published
- 2021-08-14
- Volume
- 36
- Issue
- 12
- Pages
- 2862-2873
- ISSN
- 0885-3185 (ISSN-L)
- 1531-8257
- OpenAlex
- W3188648095
- Cited by
- 77
- References
- 116
- Citations by year
- 2026: 11 · 2025: 18 · 2024: 19 · 2023: 16 · 2022: 11 · 2021: 2
- FWCI
- 7.54
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Movement Disorders (publishedVersion)
- PubMed (publishedVersion)
- Conicet (submittedVersion, cc-by)
- Funders
- Consejo Nacional de Investigaciones Científicas y Técnicas (N/A)
- COLCIENCIAS (Convocatoria Doctorado Nacional‐785)
- Fondo Nacional de Desarrollo Científico y Tecnológico (1210176, 1210195)
- H2020 Marie Skłodowska-Curie Actions (766287)
- Universidad de Antioquia (CODI 2014‐768, CODI 2017‐15530, CODI 2018‐23541)
- Universidad de Santiago de Chile (Programa Interdisciplinario de Investigación Experimental en Comunicación y Cognición (PIIECC), Facultad de Humanidades, USACH)
- Topics
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Neurology › Parkinson's Disease Mechanisms and Treatments
- Keywords
- Parkinson's disease, dysarthria, cognitive impairment, prosody, articulatory features, cognitive heterogeneity, executive function, automated speech analysis, support vector machine
- MeSH terms
- Machine Learning, Cognition, Dysarthria, Humans, Parkinson Disease, Speech, Cognitive Dysfunction
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