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

新型图神经网络检测儿童言语失语症

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-04 05:00

研究人员开发了Paediatric-HGNN，这是一种新颖的混合异构图神经网络，用于检测儿童言语中的失语症。该系统利用了上下文感知部分-整体交互网络（CaPIN），并对词语和声学片段之间的层级关系进行建模。Paediatric-HGNN 在 UCLASS 和 FluencyBank语料库上进行训练，达到了 82.4% 的加权准确率和 0.386 的典型失语症 F1 分数，为早期临床干预提供了更强大的工具。 AI

影响这项研究可能有助于更准确、更早地检测儿童的言语障碍，从而改善临床干预效果。

排序理由该集群包含一篇详细介绍新模型及其在特定任务上性能的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CL TIER_1 English(EN) · Rashini Liyanarachchi, Rachael Mackay, Alison Short, Aditya Joshi, Erik Meijering · 2026-11-04 05:00

Paediatric-HGNN：一种用于通过多尺度声学融合检测儿童言语中不流畅的混合异构图神经网络

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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Paediatric-HGNN：一种用于通过多尺度声学融合检测儿童言语中不流畅的混合异构图神经网络

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