The explanatory value of multidimensional measures of linguistic complexity for aphasia severity: evidence from AphasiaBank speech data

Vollständiger Abstract

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Aphasia is an acquired language disorder marked by substantial individual variability. Quantitative measures of connected speech may clarify how linguistic performance relates to aphasia severity. Semi-spontaneous speech samples from 22 English-speaking adults with aphasia in the APROCSA subcorpus of AphasiaBank were analyzed. Eight measures represented three domains: Mean Length of Utterance (MLU), clause density, and embedded clause ratio for syntax; type–token ratio, verb diversity, and content-word ratio for lexis; and verb marking and noun marking for morphology. Pearson correlations examined associations with Quick Aphasia Battery Overall scores. Three domain-specific regression models and one comprehensive model containing all eight measures were compared using adjusted R², AIC, and BIC.MLU (r = 0.734), clause density (r = 0.592), embedded clause ratio (r = 0.488), and verb marking (r = 0.753) were significantly positively associated with QAB Overall scores, with higher scores indicating milder aphasia. The lexical measures and noun marking were not significantly associated with QAB Overall. Among the domain-specific models, the morphological model showed the greatest explanatory power (adjusted R² = 0.523), followed by the syntactic model (adjusted R² = 0.494); the lexical model was nonsignificant. The comprehensive model provided the strongest explanatory power and best fit (adjusted R² = 0.805; AIC = 64.82; BIC = 75.73). Syntactic and morphological measures, particularly MLU and verb marking, were more sensitive to variation in aphasia severity than the lexical measures examined. Combining measures across domains provided complementary information, supporting a multidimensional approach to connected-speech assessment in aphasia.