标题： 从黑箱到生物标志物：用于解释帕金森病语音模型的稀疏自编码器 显示英文标题

标题： From Black Box to Biomarker: Sparse Autoencoders for Interpreting Speech Models of Parkinson's Disease

摘要： 语音在帕金森病(PD)等神经系统疾病中作为一种成本效益高且无创的生物标志物具有前景。 虽然在原始音频上训练的深度学习系统可以找到手工特征无法提供的细微信号，但其黑箱特性阻碍了临床应用。 为了解决这个问题，我们应用稀疏自编码器(SAEs)从基于语音的PD检测系统中揭示可解释的内部表示。 我们引入了一种新的基于掩码的激活方法，以适应小规模生物医学数据集，创建稀疏解耦的字典表示。 这些字典条目与PD语音中的典型发音缺陷有很强的关联，例如模型注意力突出的低能量区域中的减少的频谱通量和增加的频谱平坦度。 我们进一步表明，频谱通量与MRI扫描的纹状体体积测量有关，证明了SAEs揭示用于疾病监测和诊断的临床相关生物标志物的潜力。 显示英文摘要

摘要： Speech holds promise as a cost-effective and non-invasive biomarker for neurological conditions such as Parkinson's disease (PD). While deep learning systems trained on raw audio can find subtle signals not available from hand-crafted features, their black-box nature hinders clinical adoption. To address this, we apply sparse autoencoders (SAEs) to uncover interpretable internal representations from a speech-based PD detection system. We introduce a novel mask-based activation for adapting SAEs to small biomedical datasets, creating sparse disentangled dictionary representations. These dictionary entries are found to have strong associations with characteristic articulatory deficits in PD speech, such as reduced spectral flux and increased spectral flatness in the low-energy regions highlighted by the model attention. We further show that the spectral flux is related to volumetric measurements of the putamen from MRI scans, demonstrating the potential of SAEs to reveal clinically relevant biomarkers for disease monitoring and diagnosis.