ArticleScientific reports2021
Evaluating atypical language in autism using automated language measures.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 27 citations in OpenAlex.
- Context Matters: Assessing Sex and Diagnostic Differences in Autistic Children's Language Across Tasks With Differing Levels of Structure.Journal of autism and developmental disorders · 2026Article
- "Look Who's Talking": Language Patterns in Autistic and Non-Autistic Youth Across Different Conversation Partners.Journal of autism and developmental disorders · 2026Article
- Disfluencies as a Window into Pragmatic Skills in Russian-Hebrew Bilingual Autistic and Non-Autistic Children.Journal of autism and developmental disorders · 2026Article
- Using generative AI for the objective assessment of language in healthcare.Scientific reports · 2025Article
- Pre-trained artificial intelligence language model represents pragmatic language variability central to autism and genetically related phenotypes.Autism : the international journal of research and practice · 2025Article
- Reliably quantifying the severity of social symptoms in children with autism using ASDSpeech.Translational psychiatry · 2025Article
- Widespread associations between behavioral metrics and brain microstructure in ASD suggest age mediates subtypes of ASD.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Speech and language patterns in autism: Towards natural language processing as a research and clinical tool.Psychiatry research · 2024Review
- "Um" and "Uh" Usage Patterns in Children with Autism: Associations with Measures of Structural and Pragmatic Language Ability.Journal of autism and developmental disorders · 2023Article
- Automatic speaker diarization for natural conversation analysis in autism clinical trials.Scientific reports · 2023Article
- Consistency and reliability of automated language measures across expressive language samples in autism.Autism research : official journal of the International Society for Autism Research · 2023Article
- ParAlg: A Paraphasia Algorithm for Multinomial Classification of Picture Naming Errors.Journal of speech, language, and hearing research : JSLHR · 2023Article
- Evaluating the Performance of Transformer-based Language Models for Neuroatypical Language.Proceedings of COLING. International Conference on Computational Linguistics · 2022Article
- Combining voice and language features improves automated autism detection.Autism research : official journal of the International Society for Autism Research · 2022Article
- Friend matters: sex differences in social language during autism diagnostic interviews.Molecular autism · 2022Article
- Quantifying Voice Characteristics for Detecting Autism.Frontiers in psychology · 2021Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Measurement of language atypicalities in Autism Spectrum Disorder (ASD) is cumbersome and costly. Better language outcome measures are needed. Using language transcripts, we generated Automated Language Measures (ALMs) and tested their validity. 169 participants (96 ASD, 28 TD, 45 ADHD) ages 7 to 17 were evaluated with the Autism Diagnostic Observation Schedule. Transcripts of one task were analyzed to generate seven ALMs: mean length of utterance in morphemes, number of different word roots (NDWR), um proportion, content maze proportion, unintelligible proportion, c-units per minute, and repetition proportion. With the exception of repetition proportion (p [Formula: see text]), nonparametric ANOVAs showed significant group differences (p[Formula: see text]). The TD and ADHD groups did not differ from each other in post-hoc analyses. With the exception of NDWR, the ASD group showed significantly (p[Formula: see text]) lower scores than both comparison groups. The ALMs were correlated with standardized clinical and language evaluations of ASD. In age- and IQ-adjusted logistic regression analyses, four ALMs significantly predicted ASD status with satisfactory accuracy (67.9-75.5%). When ALMs were combined together, accuracy improved to 82.4%. These ALMs offer a promising approach for generating novel outcome measures.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.