Artificial Intelligence Driven Predictive Analysis of Acoustic and Linguistic Behaviors for ASD Identification

The identification of autism spectrum disorder (ASD) faces challenges due to the lack of reliable biomarkers and the subjectivity in diagnostic procedures, necessitating improved tools for objectivity and efficiency. Being a key characteristic of autism, language impairments are regarded as potential markers for identifying ASD. However, current research predominantly focuses on analyzing language characteristics in English, overlooking linguistic and contextual specificities in other resource-constrained languages. Motivated by these, we developed an artificial intelligence (AI)-based system to detect ASD, utilizing a range of acoustic and linguistic features extracted from dyadic conversations between a child and their communication partner. Validating our model on 76 English-speaking children [35 ASD and 41 typically developing (TD)] and 33 Hindi-speaking children (15 ASD and 18 TD), our extensive analysis of a diverse and comprehensive set of acoustic and linguistic speech attributes, including lexical, syntactic, semantic, and pragmatic elements revealed reliable speech attributes as predictors of ASD. This comprehensive analysis achieved a remarkable macro F1-score of approximately $\boldsymbol{\sim}$ 91.30%. We further addressed the influence of linguistic diversity on speech-based ASD assessment by examining speech behaviors in both English and the low-resource language, Hindi. Specific features such as adverbs and distinct roots contributed significantly to ASD classification in English, while the proportion of unintelligible utterances and adposition use held greater importance in Hindi. This study underscores the reliability of speech-based biomarkers in ASD assessment, emphasizing their effectiveness across diverse linguistic backgrounds and highlighting the need for language-specific research in this domain.