中文标题#
語言在從語音中早期檢測帕金森病中重要嗎？
英文标题#
Does Language Matter for Early Detection of Parkinson's Disease from Speech?
中文摘要#
使用語音樣本作為生物標誌物是檢測和監測帕金森病（PD）進展的一種有前景的方法，但文獻中在如何最佳收集和分析此類數據方面存在很大分歧。 早期研究在從語音中檢測 PD 時使用了持續元音發音（SVP）任務，而一些最近的研究則探索了更需要認知努力的任務的錄音。 為了評估語言在 PD 檢測中的作用，我們測試了具有不同數據類型和預訓練目標的預訓練模型，並發現（1）僅文本模型的表現與聲學特徵模型相當，（2）多語言 Whisper 表現優於自監督模型，而單語言 Whisper 表現較差，（3）AudioSet 預訓練提高了 SVP 的表現，但對自發語音沒有提升。 這些發現共同突顯了語言在帕金森病早期檢測中的關鍵作用。
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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