用语音分析检测慢阻肺，丹麦数据集验证了其可行性
Detecting COPD Through Speech Analysis: A Dataset of Danish Speech and Machine Learning Approach
- 采集96名丹麦人语音数据，分健康与慢阻肺组进行三类任务测试
- 基于openSMILE特征和逻辑回归模型，最高识别准确率达67%
- 为远程、无创慢阻肺筛查提供新思路，适合医疗健康领域研究者
慢性阻塞性肺病（COPD）是全球影响数百万患者的严重疾病。通过非侵入性手段早期发现可促进预防干预，改善患者生活质量与预后，而语音近年来被证实是一种有价值的生物标志物。但其在不同语言群体中的有效性仍需验证。为此，我们收集了96名丹麦参与者在朗读、咳嗽和持续元音三个语音任务中的音频数据，其中一半为不同程度的慢阻肺患者，另一半为健康对照组。随后，我们采用openSMILE特征与x-vector嵌入，评估多种基线模型表现。最佳结果使用openSMILE特征与逻辑回归模型，达到67%的分类准确率。研究结果支持语音分析作为未来慢阻肺医疗解决方案中一种非侵入、远程且可扩展的筛查工具的潜力。
原文摘要 · Abstract (English)
Chronic Obstructive Pulmonary Disease (COPD) is a serious and debilitating disease affecting millions around the world. Its early detection using non-invasive means could enable preventive interventions that improve quality of life and patient outcomes, with speech recently shown to be a valuable biomarker. Yet, its validity across different linguistic groups remains to be seen. To that end, audio data were collected from 96 Danish participants conducting three speech tasks (reading, coughing, sustained vowels). Half of the participants were diagnosed with different levels of COPD and the other half formed a healthy control group. Subsequently, we investigated different baseline models using openSMILE features and learnt x-vector embeddings. We obtained a best accuracy of 67% using openSMILE features and logistic regression. Our findings support the potential of speech-based analysis as a non-invasive, remote, and scalable screening tool as part of future COPD healthcare solutions.
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