Paediatric-HGNN: A Hybrid Heterogeneous Graph Neural Network for Detecting Disfluency in Children's Speech via Multiscale Acoustic Fusion
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Abstract (arXiv)
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 disfluencies. We introduce Paediatric-HGNN, a framework using a Context-aware Part-whole Interaction Network (CaPIN) tailored for paediatric data. Instead of conventional 1D signal modelling, our approach builds a heterogeneous graph capturing hierarchical relationships between lexical units (word nodes) and fine-grained acoustic segments (frame nodes). Trained on curated paediatric corpora (UCLASS and FluencyBank), Paediatric-HGNN achieves 82.4% weighted accuracy and a Typical Disfluency F1-score of 0.386. Modelling hierarchical lexical-acoustic interactions captures developmental "searching" behaviour, offering a more robust and interpretable tool for early clinical intervention.
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References · 5
- 2021SEP-28k: A Dataset for Stuttering Event Detection From Podcasts With People Who Stuttercited by 51
- 2019Detecting Multiple Speech Disfluencies using a Deep Residual Network with Bidirectional Long Short-Term Memorycited by 27
- 2020Controllable Time-Delay Transformer for Real-Time Punctuation Prediction and Disfluency Detectioncited by 23
- 2021StutterNet: Stuttering Detection Using Time Delay Neural Networkcited by 20
- 2025StutterCut: Uncertainty-Guided Normalised Cut for Dysfluency Segmentationcited by 1
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