Vowel production: a potential speech biomarker for early detection of dysarthria in Parkinson’s disease

Author:

Roland Virginie,Huet Kathy,Harmegnies Bernard,Piccaluga Myriam,Verhaegen Clémence,Delvaux Véronique

Abstract

ObjectivesOur aim is to detect early, subclinical speech biomarkers of dysarthria in Parkinson’s disease (PD), i.e., systematic atypicalities in speech that remain subtle, are not easily detectible by the clinician, so that the patient is labeled “non-dysarthric.” Based on promising exploratory work, we examine here whether vowel articulation, as assessed by three acoustic metrics, can be used as early indicator of speech difficulties associated with Parkinson’s disease.Study designThis is a prospective case–control study.MethodsSixty-three individuals with PD and 35 without PD (healthy controls-HC) participated in this study. Out of 63 PD patients, 43 had been diagnosed with dysarthria (DPD) and 20 had not (NDPD). Sustained vowels were recorded for each speaker and formant frequencies were measured. The analyses focus on three acoustic metrics: individual vowel triangle areas (tVSA), vowel articulation index (VAI) and the Phi index.ResultstVSA were found to be significantly smaller for DPD speakers than for HC. The VAI showed significant differences between these two groups, indicating greater centralization and lower vowel contrasts in the DPD speakers with dysarhtria. In addition, DPD and NDPD speakers had lower Phi values, indicating a lower organization of their vowel system compared to the HC. Results also showed that the VAI index was the most efficient to distinguish between DPD and NDPD whereas the Phi index was the best acoustic metric to discriminate NDPD and HC.ConclusionThis acoustic study identified potential subclinical vowel-related speech biomarkers of dysarthria in speakers with Parkinson’s disease who have not been diagnosed with dysarthria.

Publisher

Frontiers Media SA

Subject

General Psychology

Reference51 articles.

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1. Variability in Vowel Space in Parkinson's Disease: Associations With Cognitive and Motor Impairment;Journal of Speech, Language, and Hearing Research;2024-09-11

2. MFCC-Based Analysis of Vibratory Anomalies in Parkinson's Disease Detection using Sustained Vowels;2023 IEEE Afro-Mediterranean Conference on Artificial Intelligence (AMCAI);2023-12-13

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