ArticlePLOS digital health2022
Feature engineering and machine learning for computer-assisted screening of children with speech disorders.
Article in PLOS digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- A novel class-attention transformer-driven feature fusion technique-based speech disorder classification.Frontiers in medicine · 2026Article
- Application of machine learning techniques to automate speech mispronunciation detection in children and adolescents.Journal of applied oral science : revista FOB · 2026Article
- Machine Learning-Based Identification of Phonological Biomarkers for Speech Sound Disorders in Saudi Arabic-Speaking Children.Diagnostics (Basel, Switzerland) · 2025Article
- Voice as a Biomarker of Pediatric Health: A Scoping Review.Children (Basel, Switzerland) · 2024Article
- Artificial intelligence (AI)-driven technologies for managing pediatric speech and language therapy: A scoping review.Digital healthReview
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Auditory perceptual analysis (APA) is the main method for clinical assessment of speech-language deficits, which are one of the most prevalent childhood disabilities. However, results from APA are susceptible to intra- and inter-rater variabilities. There are also other limitations of manual or hand transcription-based speech disorder diagnostic methods. There is increased interest in developing automated methods that quantify speech patterns for diagnosing speech disorders in children to address these limitations. Landmark (LM) analysis is an approach that characterizes acoustic events occurring due to sufficiently precise articulatory movements. This work investigates the utilization of LMs for automatic speech disorder detection in children. Besides the LM-based features that have been proposed in existing research, we propose a set of novel knowledge-based features that have not been proposed before. A systematic study and comparison of different linear and nonlinear machine learning classification techniques based on the raw features and the proposed features is conducted to assess the effectiveness of the novel features in classifying speech disorder patients from normal speakers.
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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.