speech graph analysis of verbal fluency in children with autism spectrum disorders

Background: autism spectrum disorders (asd) are a group of developmental conditions that impair social communication and often involve repetitive behaviors. among the core features of asd, verbal impairments are prominent. verbal fluency tests are widely used neuropsychological tasks to assess language skills in children with asd. recently, speech graphs, derived from graph theory, have been employed to analyze verbal fluency performance more comprehensively.methods: this study aimed to compare speech graph features from phonemic and semantic verbal fluency tasks between children with asd and typically developing (td) peers. participants included 25 children with asd (ages 7–12 years; iq 70–85 based on the goodenough test) from an autism school in tabriz, and 30 age-matched td children from regular schools. verbal fluency was assessed using the kormi nouri fluency task with phonemic cues (a, n, m) and semantic categories (boy names, girl names, body parts, fruits, colors, kitchen utensils). spoken words were represented as nodes, and temporal links between them as edges, to construct speech graphs. standard verbal fluency scores and graph features were analyzed using independent t-tests and mann–whitney u tests.results: children with asd produced fewer words in both phonemic and semantic fluency tasks compared to td children. their speech graphs also displayed fewer nodes and edges, smaller largest connected components, lower average shortest paths and diameters, higher graph density, and reduced average total degree in comparison to td peers.conclusion: speech graph analysis offers a novel computational approach for characterizing verbal fluency deficits in children with asd. the findings suggest potential applications for developing computer-based rehabilitation tools for individuals with speech and language impairments. future studies may expand these approaches to other cognitive domains.