English(EN) Towards Personalized Federated Learning for Dysarthric Speech Recognition

个性化联邦学习改进构音障碍语音识别

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-11 12:10

研究人员开发了新的个性化联邦学习聚合策略，以改进构音障碍者的语音识别。所提出的方法侧重于基于参数和基于嵌入的平均，旨在解决联邦学习固有的异质性问题。在UASpeech和TORGO数据集上的实验表明，与基线正则化FedAvg方法相比，词错误率（WER）显著降低。 AI

影响增强了语音识别技术对言语障碍者的可访问性。

排序理由该集群包含一篇详细介绍个性化联邦学习新方法的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.AI TIER_1 English(EN) · Tao Zhong, Mengzhe Geng, Jiajun Deng, Shujie Hu, Xunying Liu · 2026-06-12 04:00

迈向用于构音障碍语音识别的个性化联邦学习

arXiv:2606.13253v1 Announce Type: cross Abstract: Speech recognition is challenging for dysarthric speakers. While federated learning (FL)-based ASR can be an effective tool for protecting privacy, it suffers from heterogeneity issues caused by speaker variability. Forcing all sp…
arXiv cs.AI TIER_1 English(EN) · Xunying Liu · 2026-06-11 12:10

面向构音障碍语音识别的个性化联邦学习

Speech recognition is challenging for dysarthric speakers. While federated learning (FL)-based ASR can be an effective tool for protecting privacy, it suffers from heterogeneity issues caused by speaker variability. Forcing all speakers to share the same model components can be s…