  Autism Spectrum Disorder (ASD) is a complex neuro-developmental challenge,
presenting a spectrum of difficulties in social interaction, communication, and
the expression of repetitive behaviors in different situations. This increasing
prevalence underscores the importance of ASD as a major public health concern
and the need for comprehensive research initiatives to advance our
understanding of the disorder and its early detection methods. This study
introduces a novel hierarchical feature fusion method aimed at enhancing the
early detection of ASD in children through the analysis of code-switched speech
(English and Hindi). Employing advanced audio processing techniques, the
research integrates acoustic, paralinguistic, and linguistic information using
Transformer Encoders. This innovative fusion strategy is designed to improve
classification robustness and accuracy, crucial for early and precise ASD
identification. The methodology involves collecting a code-switched speech
corpus, CoSAm, from children diagnosed with ASD and a matched control group.
The dataset comprises 61 voice recordings from 30 children diagnosed with ASD
and 31 from neurotypical children, aged between 3 and 13 years, resulting in a
total of 159.75 minutes of voice recordings. The feature analysis focuses on
MFCCs and extensive statistical attributes to capture speech pattern
variability and complexity. The best model performance is achieved using a
hierarchical fusion technique with an accuracy of 98.75% using a combination of
acoustic and linguistic features first, followed by paralinguistic features in
a hierarchical manner.
