Asgari Meysam, Shafran Izhak
The Center for Spoken Language Understanding, The Oregon Health & Science University, Portland, OR, USA.
IEEE Int Workshop Mach Learn Signal Process. 2010 Aug-Sep;2010:462-467. doi: 10.1109/MLSP.2010.5589118. Epub 2010 Oct 7.
Speech pathologists often describe voice quality in hypokinetic dysarthria or Parkinsonism as harsh or breathy, which has been largely attributed to incomplete closure of vocal folds. Exploiting its harmonic nature, we separate voiced portion of the speech to obtain an objective estimate of this quality. The utility of the proposed approach was evaluated on predicting 116 clinical ratings of Parkinson's disease on 82 subjects. Our results show that the information extracted from speech, elicited through 3 tasks, can predict the motor subscore (range 0 to 108) of the clinical measure, the Unified Parkinson's Disease Rating Scale, within a mean absolute error of 5.7 and a standard deviation of about 2.0. While still preliminary, our results are significant and demonstrate that the proposed computational approach has promising real-world applications such as in home-based assessment or in telemonitoring of Parkinson's disease.
言语病理学家经常将运动减少型构音障碍或帕金森症中的嗓音质量描述为粗糙或呼吸音重，这在很大程度上归因于声带闭合不完全。利用其谐波性质，我们分离出语音的浊音部分以获得对这种质量的客观估计。在预测82名受试者的116项帕金森病临床评分时，对所提出方法的效用进行了评估。我们的结果表明，通过3项任务引出的语音中提取的信息，可以在平均绝对误差为5.7且标准差约为2.0的范围内预测临床测量工具统一帕金森病评定量表的运动子评分（范围为0至108）。虽然仍处于初步阶段，但我们的结果意义重大，并表明所提出的计算方法在诸如帕金森病的家庭评估或远程监测等实际应用中具有广阔前景。
