中文标题#
语言在从语音中早期检测帕金森病中重要吗？
英文标题#
Does Language Matter for Early Detection of Parkinson's Disease from Speech?
中文摘要#
使用语音样本作为生物标志物是检测和监测帕金森病（PD）进展的一种有前景的方法，但文献中在如何最佳收集和分析此类数据方面存在很大分歧。 早期研究在从语音中检测 PD 时使用了持续元音发音（SVP）任务，而一些最近的研究则探索了更需要认知努力的任务的录音。 为了评估语言在 PD 检测中的作用，我们测试了具有不同数据类型和预训练目标的预训练模型，并发现（1）仅文本模型的表现与声学特征模型相当，（2）多语言 Whisper 表现优于自监督模型，而单语言 Whisper 表现较差，（3）AudioSet 预训练提高了 SVP 的表现，但对自发语音没有提升。 这些发现共同突显了语言在帕金森病早期检测中的关键作用。
英文摘要#
Using speech samples as a biomarker is a promising avenue for detecting and monitoring the progression of Parkinson's disease (PD), but there is considerable disagreement in the literature about how best to collect and analyze such data. Early research in detecting PD from speech used a sustained vowel phonation (SVP) task, while some recent research has explored recordings of more cognitively demanding tasks. To assess the role of language in PD detection, we tested pretrained models with varying data types and pretraining objectives and found that (1) text-only models match the performance of vocal-feature models, (2) multilingual Whisper outperforms self-supervised models whereas monolingual Whisper does worse, and (3) AudioSet pretraining improves performance on SVP but not spontaneous speech. These findings together highlight the critical role of language for the early detection of Parkinson's disease.
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