  Diagnosing autism spectrum disorder (ASD) by identifying abnormal speech
patterns from examiner-patient dialogues presents significant challenges due to
the subtle and diverse manifestations of speech-related symptoms in affected
individuals. This study presents a comprehensive approach to identify
distinctive speech patterns through the analysis of examiner-patient dialogues.
Utilizing a dataset of recorded dialogues, we extracted 40 speech-related
features, categorized into frequency, zero-crossing rate, energy, spectral
characteristics, Mel Frequency Cepstral Coefficients (MFCCs), and balance.
These features encompass various aspects of speech such as intonation, volume,
rhythm, and speech rate, reflecting the complex nature of communicative
behaviors in ASD. We employed machine learning for both classification and
regression tasks to analyze these speech features. The classification model
aimed to differentiate between ASD and non-ASD cases, achieving an accuracy of
87.75%. Regression models were developed to predict speech pattern related
variables and a composite score from all variables, facilitating a deeper
understanding of the speech dynamics associated with ASD. The effectiveness of
machine learning in interpreting intricate speech patterns and the high
classification accuracy underscore the potential of computational methods in
supporting the diagnostic processes for ASD. This approach not only aids in
early detection but also contributes to personalized treatment planning by
providing insights into the speech and communication profiles of individuals
with ASD.
