Speech pattern disorders in verbally fluent individuals with autism spectrum disorder: a machine learning analysis.

Introduction: Diagnosing Autism Spectrum Disorder (ASD) in verbally fluent individuals based on speech patterns in examiner-patient dialogues is challenging because speech-related symptoms are often subtle and heterogeneous. This study aimed to identify distinctive speech characteristics associated with ASD by analyzing recorded dialogues from the Autism Diagnostic Observation Schedule (ADOS-2). Methods: We analyzed examiner-participant dialogues from ADOS-2 Module 4 and extracted 40 speech-related features categorized into intonation, volume, rate, pauses, spectral characteristics, chroma, and duration. These acoustic and prosodic features were processed using advanced speech analysis tools and used to train machine learning models to classify ASD participants into two subgroups: those with and without A2-defined speech pattern abnormalities. Model performance was evaluated using cross-validation and standard classification metrics. Results: Using all 40 features, the support vector machine (SVM) achieved an F1-score of 84.49%. After removing Mel-Frequency Cepstral Coefficients (MFCC) and Chroma features to focus on prosodic, rhythmic, energy, and selected spectral features aligned with ADOS-2 A2 scores, performance improved, achieving 85.77% accuracy and an F1-score of 86.27%. Spectral spread and spectral centroid emerged as key features in the reduced set, while MFCC 6 and Chroma 4 also contributed significantly in the full feature set. Discussion: These findings demonstrate that a compact, diverse set of non-MFCC and selected spectral features effectively characterizes speech abnormalities in verbally fluent individuals with ASD. The approach highlights the potential of context-aware, data-driven models to complement clinical assessments and enhance understanding of speech-related manifestations in ASD.