Abstract
Conversational impairments are well known among people with autism spectrum disorder (ASD), but their measurement requires time-consuming manual annotation of language samples. Natural language processing (NLP) has shown promise in identifying semantic difficulties when compared to clinician-annotated reference transcripts. Our goal was to develop a novel measure of lexico-semantic similarity – based on recent work in natural language processing (NLP) and recent applications of pseudo-value analysis – which could be applied to transcripts of children’s conversational language, without recourse to some ground-truth reference document. We hypothesized that: (a) semantic coherence, as measured by this method, would discriminate between children with and without ASD and (b) more variability would be found in the group with ASD. We used data from 70 4- to 8-year-old males with ASD (N = 38) or typically developing (TD; N = 32) enrolled in a language study. Participants were administered a battery of standardized diagnostic tests, including the Autism Diagnostic Observation Schedule (ADOS). ADOS was recorded and transcribed, and we analyzed children’s language output during the conversation/interview ADOS tasks. Transcripts were converted to vectors via a word2vec model trained on the Google News Corpus. Pairwise similarity across all subjects and a sample grand mean were calculated. Using a leave-one-out algorithm, a pseudo-value, detailed below, representing each subject’s contribution to the grand mean was generated. Means of pseudo-values were compared between the two groups. Analyses were co-varied for nonverbal IQ, mean length of utterance, and number of distinct word roots (NDR). Statistically significant differences were observed in means of pseudo-values between TD and ASD groups (p = 0.007). TD subjects had higher pseudo-value scores suggesting that similarity scores of TD subjects were more similar to the overall group mean. Variance of pseudo-values was greater in the ASD group. Nonverbal IQ, mean length of utterance, or NDR did not account for between group differences. The findings suggest that our pseudo-value-based method can be effectively used to identify specific semantic difficulties that characterize children with ASD without requiring a reference transcript.
Highlights
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and social interaction, and patterns of restricted or repetitive behavior
While there are a number of conversational impairments that we could consider, in this study, we explore what children talk about when presented with similar conversational contexts, and if that semantic content differs between children with and without autism spectrum disorder (ASD)
When we varied the reference transcript across the set of all typically developing (TD) subjects, the mean similarity to that transcript was higher for the group of TD subjects than ASD subjects, for all but one reference transcript (Figure 1)
Summary
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and social interaction, and patterns of restricted or repetitive behavior. Repetitive speech, and perseverative interests are all features of the disorder as characterized by the DSM-5 (American Psychiatric Association, 2013), quantifying what is atypical about the language of subjects with ASD is challenging. The use of unusual words has been found to be more prevalent in speakers with ASD (Volden and Lord, 1991); coding what is unusual about these words requires linguistic and clinical expertise. Computational methods of measuring differences in language production in subjects with ASD, such as the one proposed in this work, have the potential to provide objective, quantitative measures, which could be in turn used in clinical applications, such as evaluating response to intervention
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