{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T08:31:56Z","timestamp":1791966716960,"version":"4.3.4"},"reference-count":33,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CODI at UdeA","award":["2025-80810"],"award-info":[{"award-number":["2025-80810"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Automatic evaluation of Parkinson\u2019s disease (PD) progression is an emerging topic that deserves special attention from the research community. Unobtrusive, low-cost technology is essential for monitoring PD patients in remote areas. This paper proposes the use of phonological posteriors to create models that allow the progression of dysarthria level progression to be modelled based on speech recordings. Methods: Eighteen Gated Recurrent Units (GRUs) are used to estimate an equal number of phonological classes assigned to each phoneme pronounced in a given recording. Classification models of PD vs. healthy control (HC) subjects are trained with recordings of the PC-GITA corpus. This information is used in a separate corpus, with longitudinal recordings, to evaluate whether the progression of the dysarthria level, according to the modified Frenchay Dysarthria Assessment (mFDA), is related to abnormal production of specific phonemes. Results: Strident, dental, pause, back, and continuant phonological classes are the ones that better explain dysarthria level progression within time-frames of at least two years, therefore allowing possible monitoring of disease progression. Conclusions: Speech is a low-cost biosignal that can be used to automatically assess PD progression. In particular, this study shows that such an assessment makes it possible to evaluate dysarthria level progression and to find which phonological classes are contributing the most to such a progression. We believe that the findings reported in this paper provide objective evidence about possible abnormalities in broader speech-related processes like respiration, therefore contributing a better understanding of the relationship between speech production patterns and other speech-related processes affected when suffering from PD.<\/jats:p>","DOI":"10.3390\/diagnostics16050683","type":"journal-article","created":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T11:23:45Z","timestamp":1772105025000},"page":"683","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Longitudinal Evaluation of Dysarthria Progression in Patients with Parkinson\u2019s Disease"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1880-7793","authenticated-orcid":false,"given":"Wilmar Alesander","family":"V\u00e1squez-Barrientos","sequence":"first","affiliation":[{"name":"GITA Lab, Faculty of Engineering, University of Antioquia, Medellin 050010, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3257-0134","authenticated-orcid":false,"given":"Daniel","family":"Escobar-Grisales","sequence":"additional","affiliation":[{"name":"GITA Lab, Faculty of Engineering, University of Antioquia, Medellin 050010, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0174-1452","authenticated-orcid":false,"given":"Cristian David","family":"R\u00edos-Urrego","sequence":"additional","affiliation":[{"name":"GITA Lab, Faculty of Engineering, University of Antioquia, Medellin 050010, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8507-0782","authenticated-orcid":false,"given":"Juan Rafael","family":"Orozco-Arroyave","sequence":"additional","affiliation":[{"name":"GITA Lab, Faculty of Engineering, University of Antioquia, Medellin 050010, Colombia"},{"name":"LME Lab, University of Erlangen, 91058 Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,26]]},"reference":[{"key":"ref_1","first-page":"135","article-title":"Delivering multidisciplinary rehabilitation care in Parkinson\u2019s disease: An international consensus statement","volume":"14","author":"Goldman","year":"2024","journal-title":"J. Park. Dis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104503","DOI":"10.1016\/j.compbiomed.2021.104503","article-title":"Monitoring Parkinson\u2019s disease progression based on recorded speech with missing ordinal responses and replicated covariates","volume":"134","author":"Naranjo","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1002\/mdc3.13262","article-title":"Progression in parkinson\u2019s disease: Variation in motor and non-motor symptoms severity and predictors of decline in cognition, motor function, disability, and health-related quality of life as assessed by two different methods","volume":"8","author":"Bugalho","year":"2021","journal-title":"Mov. Disord. Clin. Pract."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"21329","DOI":"10.3390\/s141121329","article-title":"PERFORM: A system for monitoring, assessment and management of patients with Parkinson\u2019s disease","volume":"14","author":"Tzallas","year":"2014","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e1986","DOI":"10.1212\/WNL.0000000000012826","article-title":"Trends in mortality from Parkinson disease in the United States, 1999\u20132019","volume":"97","author":"Rong","year":"2021","journal-title":"Neurology"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"137","DOI":"10.2217\/nmt-2019-0037","article-title":"Apkinson: The smartphone application for telemonitoring Parkinson\u2019s patients through speech, gait, and hands movement","volume":"10","author":"Klumpp","year":"2020","journal-title":"Neurodegener. Dis. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"205","DOI":"10.3233\/NRE-2005-20307","article-title":"Speech treatment for Parkinson\u2019s disease","volume":"20","author":"Trail","year":"2005","journal-title":"NeuroRehabilitation"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wodzinski, M., Skalski, A., Hemmerling, D., Orozco-Arroyave, J.R., and N\u00f6th, E. (2019). Deep learning approach to Parkinson\u2019s disease detection using voice recordings and convolutional neural network dedicated to image classification. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE.","DOI":"10.1109\/EMBC.2019.8856972"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"108228","DOI":"10.1016\/j.compbiolchem.2024.108228","article-title":"Comprehensive review of literature on Parkinson\u2019s disease diagnosis","volume":"113","author":"Pradeep","year":"2024","journal-title":"Comput. Biol. Chem."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1038\/s41598-017-00047-5","article-title":"Automated analysis of connected speech reveals early biomarkers of Parkinson\u2019s disease in patients with rapid eye movement sleep behaviour disorder","volume":"7","author":"Hlavnicka","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Roland, V., Huet, K., Harmegnies, B., Piccaluga, M., Verhaegen, C., and Delvaux, V. (2023). Vowel production: A potential speech biomarker for early detection of dysarthria in Parkinson\u2019s disease. Front. Psychol., 14.","DOI":"10.3389\/fpsyg.2023.1129830"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1044\/1092-4388(2013\/12-0262)","article-title":"Vowel acoustics in dysarthria: Speech disorder diagnosis and classification","volume":"57","author":"Lansford","year":"2014","journal-title":"J. Speech Lang. Hear. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3749","DOI":"10.1044\/2022_JSLHR-21-00539","article-title":"Voice alterations, dysarthria, and respiratory derangements in patients with Parkinson\u2019s disease","volume":"65","author":"Olivares","year":"2022","journal-title":"J. Speech Lang. Hear. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1044\/2017_AJSLP-17-0032","article-title":"The effects of intensive speech treatment on conversational intelligibility in Spanish speakers with Parkinson\u2019s disease","volume":"27","author":"Goudarzi","year":"2018","journal-title":"Am. J. Speech-Lang. Pathol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1109\/TBME.2012.2183367","article-title":"Novel speech signal processing algorithms for high-accuracy classification of Parkinson\u2019s disease","volume":"59","author":"Tsanas","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_16","unstructured":"Orozco-Arroyave, J.R. (2015). Analysis of Speech of People with Parkinson\u2019s Disease, Logos Vergal."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.dsp.2017.07.004","article-title":"NeuroSpeech: An open-source software for Parkinson\u2019s speech analysis","volume":"77","author":"Arora","year":"2018","journal-title":"Digit. Signal Process."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"V\u00e1squez-Correa, J., Klumpp, P., Orozco-Arroyave, J., and N\u00f6th, E. (2019). Phonet: A Tool Based on Gated Recurrent Neural Networks to Extract Phonological Posteriors from Speech. Proceedings of the Interspeech, ISCA.","DOI":"10.21437\/Interspeech.2019-1405"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"101321","DOI":"10.1016\/j.csl.2021.101321","article-title":"The Phonetic Footprint of Parkinson\u2019s Disease","volume":"72","author":"Klumpp","year":"2021","journal-title":"Comput. Speech Lang."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"19066","DOI":"10.1038\/s41598-019-55271-y","article-title":"Phonetic relevance and phonemic grouping of speech in the automatic detection of Parkinson\u2019s Disease","volume":"9","author":"Dehak","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1109\/TASLP.2022.3212829","article-title":"Automatic assessment of Parkinson\u2019s disease using speech representations of phonation and articulation","volume":"31","author":"Liu","year":"2022","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1109\/TAFFC.2015.2457417","article-title":"The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing","volume":"7","author":"Eyben","year":"2016","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Escobar-Grisales, D., Arias-Vergara, T., Rios-Urrego, C.D., N\u00f6th, E., Garc\u00eda, A.M., and Orozco-Arroyave, J.R. (2023). An automatic multimodal approach to analyze linguistic and acoustic cues on Parkinson\u2019s disease patients. Proceedings of the Interspeech, ISCA.","DOI":"10.21437\/Interspeech.2023-2287"},{"key":"ref_24","first-page":"389195","article-title":"Progression of Voice and Speech Impairment in the Course of Parkinson\u2019s Disease: A Longitudinal Study","volume":"2013","author":"Skodda","year":"2013","journal-title":"Park. Dis."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.specom.2018.05.007","article-title":"Speaker models for monitoring Parkinson\u2019s disease progression considering different communication channels and acoustic conditions","volume":"101","year":"2018","journal-title":"Speech Commun."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2862","DOI":"10.1002\/mds.28751","article-title":"Cognitive determinants of dysarthria in Parkinson\u2019s disease: An automated machine learning approach","volume":"36","author":"Garcia","year":"2021","journal-title":"Mov. Disord."},{"key":"ref_27","unstructured":"Orozco-Arroyave, J.R., Arias-Londo\u00f1o, J.D., Vargas-Bonilla, J.F., Gonzalez-R\u00e1tiva, M.C., and N\u00f6th, E. (2014, January 26\u201331). New Spanish speech corpus database for the analysis of people suffering from Parkinson\u2019s disease. Proceedings of the LREC, Reykjavik, Iceland."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2129","DOI":"10.1002\/mds.22340","article-title":"Movement Disorder Society-sponsored revision of the Unified Parkinson\u2019s Disease Rating Scale (MDS-UPDRS): Scale presentation and clinimetric testing results","volume":"23","author":"Goetz","year":"2008","journal-title":"Mov. Disord."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.jcomdis.2018.08.002","article-title":"Towards an automatic evaluation of the dysarthria level of patients with Parkinson\u2019s disease","volume":"76","author":"Bocklet","year":"2018","journal-title":"J. Commun. Disord."},{"key":"ref_30","unstructured":"Brinkman, J. (2014). A Longitudinal Study of Parkinsonian Speech Characteristics. [Master\u2019s Thesis, Communicative Sciences and Disorders]."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.1044\/2022_JSLHR-21-00485","article-title":"Longitudinal Effects of Parkinson\u2019s Disease on Speech Breathing During an Extemporaneous Connected Speech Task","volume":"65","author":"Anspach","year":"2022","journal-title":"J. Speech Lang. Hear. Res."},{"key":"ref_32","first-page":"100372","article-title":"Does speech in patients with different Parkinson\u2019s disease subtypes decline over time?","volume":"13","author":"Zardin","year":"2025","journal-title":"Clin. Park. Relat. Disord."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wright, H., and Aharonson, V. (2025). Vocal Feature Changes for Monitoring Parkinson\u2019s Disease Progression\u2014A Systematic Review. Brain Sci., 15.","DOI":"10.3390\/brainsci15030320"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/5\/683\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T11:37:12Z","timestamp":1772105832000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/5\/683"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,26]]},"references-count":33,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["diagnostics16050683"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16050683","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,26]]}}}