Measuring Articulatory Patterns in Amyotrophic Lateral Sclerosis Using a Data-Driven Articulatory Consonant Distinctiveness Space Approach

Abstract

Purpose: The aim of this study was to leverage data-driven approaches, including a novel articulatory consonant distinctiveness space (ACDS) approach, to better understand speech motor control in amyotrophic lateral sclerosis (ALS). Method: Electromagnetic articulography was used to record tongue and lip movement data during the production of 10 consonants from healthy controls ( n = 15) and individuals with ALS ( n = 47). To assess phoneme distinctness, speech data were analyzed using two classification algorithms, Procrustes matching (PM) and support vector machine (SVM), and the area/volume of the ACDS. Pearson's correlation coefficient was used to examine the relationship between bulbar impairment and the ACDS. Analysis of variance was used to examine the effects of bulbar impairment on consonant distinctiveness and consonant classification accuracies in clinical subgroups. Results: There was a significant relationship between the ACDS and intelligible speaking rate (area, p = .003; volume, p = .010), and the Amyotrophic Lateral Sclerosis Functional Rating Scale–Revised (ALSFRS-R) bulbar subscore (area, p = .009; volume, p = .027). Consonant classification performance followed a consistent pattern with bulbar severity, where consonants produced by speakers with more severe ALS were classified less accurately (SVM = 75.27%; PM = 74.54%) than the healthy, asymptomatic, and mild–moderate groups. In severe ALS, area of the ACDS was significantly condensed compared to both asymptomatic ( p = .004) and mild–moderate ( p = .013) groups. There was no statistically significant difference in area between the severe ALS group and healthy speakers ( p = .292). Conclusions: Our comprehensive approach is sensitive to early oromotor changes in response due to disease progression. The preserved articulatory consonant space may capture the use of compensatory adaptations to counteract influences of neurodegeneration. Supplemental Material: https://doi.org/10.23641/asha.22044320