自适应自监督语音表示用于帕金森病跨语言构音障碍检测
Adapting Self-Supervised Speech Representations for Cross-lingual Dysarthria Detection in Parkinson's Disease
AI总结：
针对构音障碍数据稀缺导致跨语言检测困难，提出基于质心的表示级语言迁移方法，对齐自监督语音表示，在捷克语、德语和西班牙语帕金森病语音数据集上显著提升跨语言检测敏感性和F1值。
摘要：
构音障碍语音数据的有限可用性使得跨语言检测成为一个重要但具有挑战性的问题。一个关键困难是语音表示通常编码了语言相关的结构，这可能会混淆构音障碍检测。我们提出了一种表示级语言迁移（LS），该方法使用基于健康对照语音估计的质心向量适应，将源语言的自监督语音表示与目标语言分布对齐。我们在捷克语、德语和西班牙语的帕金森病语音数据集的口头DDK录音上，在跨语言和多语言设置下评估了该方法。LS在跨语言设置中显著提高了敏感性和F1，而在多语言设置中取得了较小但一致的改进。表示分析进一步表明，LS减少了嵌入空间中的语言身份，支持了LS去除语言相关结构的解释。
英文摘要：
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 dysarthria detection. We propose a representation-level language shift (LS) that aligns source-language self-supervised speech representations with the target-language distribution using centroid-based vector adaptation estimated from healthy-control speech. We evaluate the approach on oral DDK recordings from Parkinson's disease speech datasets in Czech, German, and Spanish under both cross-lingual and multilingual settings. LS substantially improves sensitivity and F1 in cross-lingual settings, while yielding smaller but consistent gains in multilingual settings. Representation analysis further shows that LS reduces language identity in the embedding space, supporting the interpretation that LS removes language-dependent structure.
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