Unsupervised Machine Learning Reveals Heterogeneous Acoustic Phenotypes in Autistic Adult Speech

Autistic speech is highly heterogeneous, yet group-level comparisons may obscure meaningful individual acoustic patterns. This study used unsupervised machine learning to identify data-driven acoustic profiles in native speakers of Cypriot Greek, including autistic and neurotypical adults. Participants produced disyllabic pseudowords across controlled phonetic and stress conditions. Sixteen acoustic measures, including fundamental frequency, formants, duration, cepstral peak prominence, Mel-frequency cepstral coefficients, jitter, shimmer, harmonics-to-noise ratio, and intensity, were summarized at the participant level and normalized appropriately. Principal component analysis retained eight components explaining 81.4% of total variance, followed by k-means clustering. A three-cluster solution provided the best silhouette coefficient among tested solutions and showed good bootstrap stability. Cluster membership was significantly associated with diagnostic group: one profile was exclusively autistic, one was relatively balanced, and one was predominantly neurotypical. The dominant acoustic dimension was driven primarily by voice-quality and spectral measures, particularly cepstral peak prominence, intensity, shimmer, harmonics-to-noise ratio, and jitter, whereas pitch and formant measures contributed comparatively little. These findings demonstrate that unsupervised acoustic profiling can reveal stable, diagnostically relevant speech phenotypes that are not captured by conventional binary group comparisons, highlighting substantial within-group heterogeneity in autistic speech and supporting more individualized approaches to characterizing vocal variation.