School of Chinese Language and Literature, Nanjing Normal University, Nanjing, China.
Comput Intell Neurosci. 2022 May 9;2022:3287068. doi: 10.1155/2022/3287068. eCollection 2022.
To investigate the effectiveness of identifying patients with Parkinson's disease (PD) from speech signals, various acoustic parameters including prosodic and segmental features are extracted from speech and then the random forest classification (RF) algorithm based on these acoustic parameters is applied to diagnose early-stage PD patients. To validate the proposed method of RF algorithm in early-stage PD identification, this study compares the accuracy rate of RF with that of neurologists' judgments based on auditory test outcomes, and the results clearly show the superiority of the proposed method over its rival. Random forest algorithm based on speech can improve the accuracy of patients' identification, which provides an efficient auxiliary method in the early diagnosis of PD patients.
为了探究从语音信号中识别帕金森病（Parkinson's disease，PD）患者的有效性，本研究从语音中提取了各种声学参数，包括韵律和音段特征，然后应用基于这些声学参数的随机森林分类（random forest classification，RF）算法来诊断早期 PD 患者。为了验证 RF 算法在早期 PD 识别中的有效性，本研究将 RF 的准确率与基于听觉测试结果的神经科医生判断进行了比较，结果清楚地表明了该方法的优越性。基于语音的随机森林算法可以提高患者识别的准确性，为 PD 患者的早期诊断提供了一种有效的辅助方法。
