AI模型利用跨语言和迁移学习解决构音障碍严重程度问题

报道来源 [3]

1. arXiv cs.CL TIER_1 English(EN) · Abner Hernandez, Eunjung Yeo, Kwanghee Choi, Chin-Jou Li, Zhengjun Yue, Rohan Kumar Das, Jan Rusz, Mathew Magimai Doss, Juan Rafael Orozco-Arroyave, Tom\'as Arias-Vergara, Andreas Maier, Elmar N\"oth, David R. Mortensen, David Harwath, Paula Andrea Perez… · 2026-06-25 04:00

   Adapting Self-Supervised Speech Representations for Cross-lingual Dysarthria Detection in Parkinson's Disease

   arXiv:2603.22225v3 Announce Type: replace Abstract: The limited availability of dysarthric speech data makes cross-lingual detection an important but challenging problem. A key difficulty is that speech representations often encode language-dependent structure that can confound d…

2. arXiv cs.CL TIER_1 English(EN) · Myoung-Wan Koo · 2026-06-22 06:51

   Cross-lingual Retrieval-Augmented Classification for Dysarthria Severity Assessment

   Automatic dysarthria severity assessment is limited by the scarcity of labeled pathological speech data. To address this, we propose Cross-lingual Retrieval-Augmented Classification (CRAC), which leverages speech from a different language via an align-retrieve-fuse pipeline. Supe…

3. arXiv cs.AI TIER_1 English(EN) · Shrikanth Narayanan · 2026-06-20 18:19

   DSSCNet：一种用于跨语料库构音障碍言语严重程度分类的迁移学习框架

   Dysarthric speech severity classification is challenging due to speaker variability, class imbalance, and limited datasets. This study introduces DSSCNet, a deep learning model that employs transfer learning and multi-corpus learning to enhance speaker-independent classification.…