ArticleFrontiers in psychology2021
Quantifying Voice Characteristics for Detecting Autism.
Article in Frontiers in psychology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Methods in Early Detection of Autism Spectrum Disorder: A DSM-5 Criterion-Based Systematic Review.Autism research : official journal of the International Society for Autism Research · 2026Pooled it
- Voice as a predictive signal: protocol for a scoping review of AI in early childhood development.BMJ open · 2026Article
- Use of Automation Technologies and Data Mining in Speech Recognition for Autism.Brain and behavior · 2026Review
- Using generative AI for the objective assessment of language in healthcare.Scientific reports · 2025Article
- A Scoping Review of AI-Based Approaches for Detecting Autism Traits Using Voice and Behavioral Data.Bioengineering (Basel, Switzerland) · 2025Review
- A Review on Autism Spectrum Disorder Screening by Artificial Intelligence Methods.Journal of autism and developmental disorders · 2025Review
- [Artificial intelligence in preventive medicine for children and adolescents-applications and acceptance].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025Review
- The Sound of Emotional Prosody: Nearly 3 Decades of Research and Future Directions.Perspectives on psychological science : a journal of the Association for Psychological Science · 2025Article
- The Human Voice as a Digital Health Solution Leveraging Artificial Intelligence.Sensors (Basel, Switzerland) · 2025Review
- Acoustic features of emotional expression in 5-year-old children with autism spectrum disorder.Frontiers in psychiatry · 2025Article
- Speech and language patterns in autism: Towards natural language processing as a research and clinical tool.Psychiatry research · 2024Review
- Voice as a Biomarker of Pediatric Health: A Scoping Review.Children (Basel, Switzerland) · 2024Article
- Exploring Motor Speech Disorders in Low and Minimally Verbal Autistic Individuals: An Auditory-Perceptual Analysis.American journal of speech-language pathology · 2024Article
- Can Natural Speech Prosody Distinguish Autism Spectrum Disorders? A Meta-Analysis.Behavioral sciences (Basel, Switzerland) · 2024Review
- Detecting autism from picture book narratives using deep neural utterance embeddings.International journal of language & communication disorders · 2022Article
- Combining voice and language features improves automated autism detection.Autism research : official journal of the International Society for Autism Research · 2022Article
- The State of Natural Language Sampling in Autism Research: A Scoping Review.Autism & developmental language impairmentsReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The presence of prosodic anomalies in autistic is recognized by experienced clinicians but their quantitative analysis is a cumbersome task beyond the scope of typical pen and pencil assessment. This paper proposes an automatic approach allowing to tease apart various aspects of prosodic abnormalities and to translate them into fine-grained, automated, and quantifiable measurements. Using a harmonic model (HM) of voiced signal, we isolated the harmonic content of speech and computed a set of quantities related to harmonic content. Employing these measures, along with standard speech measures such as loudness, we successfully trained machine learning models for distinguishing individuals with autism from those with typical development (TD). We evaluated our models empirically on a task of detecting autism on a sample of 118 youth (90 diagnosed with autism and 28 controls; mean age: 10.9 years) and demonstrated that these models perform significantly better than a chance model. Voice and speech analyses could be incorporated as novel outcome measures for treatment research and used for early detection of autism in preverbal infants or toddlers at risk of autism.
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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.