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New Graph Neural Network Detects Speech Disfluencies in Children

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Paediatric-HGNN, a novel hybrid heterogeneous graph neural network designed to detect disfluencies in children's speech. This system utilizes a Context-aware Part-whole Interaction Network (CaPIN) and models hierarchical relationships between words and acoustic segments. Trained on the UCLASS and FluencyBank corpora, Paediatric-HGNN achieved 82.4% weighted accuracy and a Typical Disfluency F1-score of 0.386, offering a more robust tool for early clinical intervention. AI

IMPACT This research could lead to more accurate and earlier detection of speech disorders in children, improving clinical intervention outcomes.

RANK_REASON The cluster contains an academic paper detailing a new model and its performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Graph Neural Network Detects Speech Disfluencies in Children

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The cluster contains an academic paper detailing a new model and its performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Rashini Liyanarachchi, Rachael Mackay, Alison Short, Aditya Joshi, Erik Meijering ·

    Paediatric-HGNN: A Hybrid Heterogeneous Graph Neural Network for Detecting Disfluency in Children's Speech via Multiscale Acoustic Fusion

    arXiv:2606.08210v2 Announce Type: replace-cross Abstract: Automated stuttering detection (ASD) systems struggle with paediatric speech due to high acoustic variability in developing voices and the subtle distinction between pathological stuttering and typical developmental disflu…

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