ArticleScientific reports2022
Automatic vocalisation-based detection of fragile X syndrome and Rett syndrome.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From data to discovery: Technology propels speech-language research and theory-building in developmental science.Neuroscience and biobehavioral reviews · 2025Pooled it
- Trajectory of skill acquisition, loss, and regain in females with classic Rett syndrome.Journal of neurodevelopmental disorders · 2026Article
- Trajectory of skill acquisition, loss, and regain in females with classic Rett syndrome.Research square · 2025Article
- Editorial: Artificial intelligence for child health and wellbeing.Frontiers in digital health · 2025Article
- Voice as a Biomarker of Pediatric Health: A Scoping Review.Children (Basel, Switzerland) · 2024Article
- Focused review on artificial intelligence for disease detection in infants.Frontiers in digital health · 2024Review
- Vocal Characteristics of Infants at Risk for Speech Motor Involvement: A Scoping Review.Journal of speech, language, and hearing research : JSLHR · 2023Article
- HEAR4Health: a blueprint for making computer audition a staple of modern healthcare.Frontiers in digital health · 2023Review
- Emerging Verbal Functions in Early Infancy: Lessons from Observational and Computational Approaches on Typical Development and Neurodevelopmental Disorders.Advances in neurodevelopmental disorders · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Fragile X syndrome (FXS) and Rett syndrome (RTT) are developmental disorders currently not diagnosed before toddlerhood. Even though speech-language deficits are among the key symptoms of both conditions, little is known about infant vocalisation acoustics for an automatic earlier identification of affected individuals. To bridge this gap, we applied intelligent audio analysis methodology to a compact dataset of 4454 home-recorded vocalisations of 3 individuals with FXS and 3 individuals with RTT aged 6 to 11 months, as well as 6 age- and gender-matched typically developing controls (TD). On the basis of a standardised set of 88 acoustic features, we trained linear kernel support vector machines to evaluate the feasibility of automatic classification of (a) FXS vs TD, (b) RTT vs TD, (c) atypical development (FXS+RTT) vs TD, and (d) FXS vs RTT vs TD. In paradigms (a)-(c), all infants were correctly classified; in paradigm (d), 9 of 12 were so. Spectral/cepstral and energy-related features were most relevant for classification across all paradigms. Despite the small sample size, this study reveals new insights into early vocalisation characteristics in FXS and RTT, and provides technical underpinnings for a future earlier identification of affected individuals, enabling earlier intervention and family counselling.
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