Segment-dependent dynamics in predicting parkinson's disease
Segment-dependent dynamics in predicting parkinson's disease
复制标题
预测帕金森病的分段依赖性动态
DOI：
10.21437/interspeech.2015-187
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
D. Mehta
中科院分区：
文献类型：
--
作者：
J. Williamson;T. Quatieri;Brian S. Helfer;Joseph Perricone;Satrajit S. Ghosh;G. Ciccarelli;D. Mehta
早期准确检测帕金森病可能有助于可能的干预和康复。因此，需要简单的非侵入性生物标志物来确定严重程度。在这项研究中，一组新的声学语音生物标志物的介绍和融合与传统的功能，用于预测帕金森氏病的临床评估。我们介绍了声学生物标志物，反映段依赖的语音产生组件的变化，在潜在的神经运动，发音和韵律的大脑中心的语音干扰的动机。这种变化发生在语音和更大的时间尺度上，包括共振峰频率和音高轨迹、音素持续时间及其发生频率以及时间波形结构的多尺度扰动。我们还介绍了发音功能的神经计算模型的基础上的语音生产，方向到速度的发音（DIVA）模型。所使用的数据库来自Interspeech 2015 Computational Paralinguistic Challenge。通过融合传统的和新的语音功能，我们得到的斯皮尔曼预测分数和临床评估之间的相关性r = 0.63的训练集（四重交叉验证），r = 0.70的一个保持了发展集，和r = 0.97的一个保持了测试集。
Early, accurate detection of Parkinson’s disease may aid in possible intervention and rehabilitation. Thus, simple noninvasive biomarkers are desired for determining severity. In this study, a novel set of acoustic speech biomarkers are introduced and fused with conventional features for predicting clinical assessment of Parkinson’s disease. We introduce acoustic biomarkers reflecting the segment dependence of changes in speech production components, motivated by disturbances in underlying neural motor, articulatory, and prosodic brain centers of speech. Such changes occur at phonetic and larger time scales, including multi-scale perturbations in formant frequency and pitch trajectories, in phoneme durations and their frequency of occurrence, and in temporal waveform structure. We also introduce articulatory features based on a neural computational model of speech production, the Directions into Velocities of Articulators (DIVA) model. The database used is from the Interspeech 2015 Computational Paralinguistic Challenge. By fusing conventional and novel speech features, we obtain Spearman correlations between predicted scores and clinical assessments of r = 0.63 on the training set (four-fold cross validation), r = 0.70 on a held-out development set, and r = 0.97 on a held-out test set.