用语音识别帕金森药效状态，准确率达88.2%。
On the Relevance of Clinical Assessment Tasks for the Automatic Detection of Parkinson's Disease Medication State from Speech
- 采用无监督语音表征，不依赖特定说话人
- 连续语音与语调特征提升识别效果，F1达88.2%
- 适合临床辅助诊断，降低患者录音负担
帕金森病患者药物状态的自动识别可辅助医生监测与个性化治疗，并研究药物对运动症状的影响。本文探索语音作为非侵入性、易获取的生物标志物，提出一种独立于说话人的新方法。尽管传统机器学习模型表现良好，但自监督语音表示显著优于基于知识的声学特征。在多种语音评估任务中，语调和连续语音对区分药物状态具有关键作用，最终达到88.2%的F1分数。该成果有望简化临床工作流程，减少患者语音记录负担。
原文摘要 · Abstract (English)
The automatic identification of medication states of Parkinson's disease (PD) patients can assist clinicians in monitoring and scheduling personalized treatments, as well as studying the effects of medication in alleviating the motor symptoms that characterize the disease. This paper explores speech as a non-invasive and accessible biomarker for identifying PD medication states, introducing a novel approach that addresses this task from a speaker-independent perspective. While traditional machine learning models achieve competitive results, self-supervised speech representations prove essential for optimal performance, significantly surpassing knowledge-based acoustic descriptors. Experiments across diverse speech assessment tasks highlight the relevance of prosody and continuous speech in distinguishing medication states, reaching an F1-score of 88.2%. These findings may streamline clinicians' work and reduce patient effort in voice recordings.
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