Emily Ferguson


2018

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Oral-Motor and Lexical Diversity During Naturalistic Conversations in Adults with Autism Spectrum Disorder
Julia Parish-Morris | Evangelos Sariyanidi | Casey Zampella | G. Keith Bartley | Emily Ferguson | Ashley A. Pallathra | Leila Bateman | Samantha Plate | Meredith Cola | Juhi Pandey | Edward S. Brodkin | Robert T. Schultz | Birkan Tunç
Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by impaired social communication and the presence of restricted, repetitive patterns of behaviors and interests. Prior research suggests that restricted patterns of behavior in ASD may be cross-domain phenomena that are evident in a variety of modalities. Computational studies of language in ASD provide support for the existence of an underlying dimension of restriction that emerges during a conversation. Similar evidence exists for restricted patterns of facial movement. Using tools from computational linguistics, computer vision, and information theory, this study tests whether cognitive-motor restriction can be detected across multiple behavioral domains in adults with ASD during a naturalistic conversation. Our methods identify restricted behavioral patterns, as measured by entropy in word use and mouth movement. Results suggest that adults with ASD produce significantly less diverse mouth movements and words than neurotypical adults, with an increased reliance on repeated patterns in both domains. The diversity values of the two domains are not significantly correlated, suggesting that they provide complementary information.

2016

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Building Language Resources for Exploring Autism Spectrum Disorders
Julia Parish-Morris | Christopher Cieri | Mark Liberman | Leila Bateman | Emily Ferguson | Robert T. Schultz
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that would benefit from low-cost and reliable improvements to screening and diagnosis. Human language technologies (HLTs) provide one possible route to automating a series of subjective decisions that currently inform “Gold Standard” diagnosis based on clinical judgment. In this paper, we describe a new resource to support this goal, comprised of 100 20-minute semi-structured English language samples labeled with child age, sex, IQ, autism symptom severity, and diagnostic classification. We assess the feasibility of digitizing and processing sensitive clinical samples for data sharing, and identify areas of difficulty. Using the methods described here, we propose to join forces with researchers and clinicians throughout the world to establish an international repository of annotated language samples from individuals with ASD and related disorders. This project has the potential to improve the lives of individuals with ASD and their families by identifying linguistic features that could improve remote screening, inform personalized intervention, and promote advancements in clinically-oriented HLTs.

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Exploring Autism Spectrum Disorders Using HLT
Julia Parish-Morris | Mark Liberman | Neville Ryant | Christopher Cieri | Leila Bateman | Emily Ferguson | Robert Schultz
Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology