发表机构
Radboud University（拉德堡德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究评估四种开源语音基础模型的预训练嵌入在跨语言多类别帕金森病严重程度分类中的表现，发现其可实现有效迁移但性能受数据与策略影响，需加强特征提取与可解释性。
AI 中文摘要
帕金森病（PD）常通过言语障碍表现出来，这使得早期诊断和病程追踪能够采用便捷、非侵入性且成本低廉的严重程度评估方法。尽管语音基础模型（SFMs）取得了进展，但由于标注数据有限、缺乏可解释方法以及不同语言和数据集间的差异性，其在帕金森病严重程度多类别分类中的跨语言泛化能力仍未得到充分探索。在本研究中，我们在零样本和k样本跨语言设置下，评估了来自四个最先进的开源SFMs的预训练嵌入，在三个数据集上进行帕金森病严重程度的多类别分类。我们的结果表明，预训练语音嵌入能够实现有意义的跨语言迁移，尽管性能对数据集属性、预处理和适应策略较为敏感。在这些条件下，与说话者间变异性和非典型言语模式相关的错误分类，凸显了在帕金森病严重程度评估中需要更稳健的特征提取和建模方法，同时强调了可解释性对于获得可靠临床见解的重要性。
英文摘要
Parkinson's disease (PD) often manifests through speech impairments, facilitating accessible, non-invasive, and cost-effective severity assessment for early diagnosis and progression tracking. Despite advances in speech foundation models (SFMs), their cross-lingual generalization for PD severity multi-class classification remains underexplored due to limited labeled data, a lack of explainable methods and variability across languages and datasets. In this work, we evaluate pre-trained embeddings from four state-of-the-art open-source SFMs across three datasets in zero-shot and k-shot cross-lingual settings for multi-class PD severity assessment. Our results show that pre-trained speech embeddings enable meaningful cross-lingual transfer, although performance is sensitive to dataset properties, preprocessing, and adaptation strategy. Misclassifications under these conditions related to inter-speaker variability and atypical speech patterns highlight the need for more robust feature extraction and modeling for PD severity assessment while emphasizing the importance of explainability for reliable clinical insights.
CommentsAccepted and published at IEEE SLT 2026 - IEEE Spoken Language Technology 2026. OneVoice-MSD 2026: Multilingual Speech Technologies for Motor Speech Disorders. https://attend.ieee.org/slt-2026/ Please cite the conference version
