SPASHT: Semantic and Pragmatic Speech Features for Automatic Assessment of Autism

Language and communication impairments are considered one of the core features of autism spectrum disorder (ASD). Quantifying the language atypicalities in autism is a challenging task. Prior works have explored acoustic, and text-based features to assess children’s language and communicative behaviours and have shown their relevance in the diagnosis of autism. In this work, we explore the semantic and pragmatic language features in children with autism (CwA) to understand their significance in the diagnosis of autism. We use natural language processing (NLP) and machine learning (ML) techniques to automatically extract relevant features and detect the existence of speech behaviours such as echolalia, semantic coherence, repetitive language, etc. We further analyse their correlation with the clinical diagnosis of autism. We conducted validation experiments on the transcripts of 76 children (35 ASD and 41 TD) extracted from the CHILDES databank. Our analysis shows that the semantic and pragmatic language features are representative candidates for autism diagnosis and are found to complement the syntactic and lexical features in the classification of CwA with an accuracy of 94%. Further, these features being more coherent and relatable to the standard diagnostic tools improves the interpretability of the diagnostic predictions made using speech signals.