Acoustic Analysis of Vowel Production in Adults with Autism Spectrum Disorder: Beyond the Notion of a Unitary Autistic Voice

Speech in autism spectrum disorder (ASD) carries distinctive acoustic signatures that can offer valuable insight into the nature of autistic communication and its identification. Among these, vowel production remains insufficiently understood, despite its central role in speech intelligibility. This study investigates whether ASD is associated with systematic differences in vowel production. Eighteen Cypriot Greek adults with ASD and 18 peers with neurotypical development (ND), comparable in age, gender, education, nonverbal IQ, and verbal fluency, completed a controlled reading task. Participants produced disyllabic pseudowords embedding the five Greek vowels across four stress-syllable contexts. Acoustic analyses measured vowel-space organization, static spectral properties (F0, F1, F2, F3), dynamic trajectories (ΔF0, ΔF1, ΔF2, ΔF3), duration, and voice-quality indices (jitter, shimmer, harmonic-to-noise ratio [HNR], intensity). Bayesian models were used to evaluate group and vowel-specific differences. The results revealed a larger vowel-space area and greater vowel-space dispersion in the ASD group relative to the ND group, indicating a more expanded and dispersed acoustic vowel system. Group differences in individual acoustic measures were mostly selective rather than global: the clearest effects emerged in vowel-specific patterns of pitch, formants, and some dynamic formant measures. By contrast, duration, jitter, shimmer, and intensity did not show robust vowel-specific group differences. Among voice-quality measures, HNR showed the most consistent group difference, with ASD speakers showing higher HNR across all vowels. These findings challenge the notion of a single, uniform autistic voice, instead demonstrating that autism-related speech differences are multidimensional, vowel-specific, and language-sensitive. They therefore underscore the critical importance of segment-focused, cross-linguistically grounded approaches for advancing theory, assessment, and future speech-based identification in autism research.