Abstract Parkinson’s disease is a progressive neurodegenerative disorder often accompanied by impairment in articulation, phonation, prosody and fluency of speech. In fact, speech impairment is one of the earliest Parkinson’s disease symptoms, and may be used for early diagnosis. We present an experimental study of identification of Parkinson’s disease and assessment of disease progress from speech using Gaussian processes, which is further combined with Automatic Relevance Determination (ARD) for efficient feature selection. Hyperparameters of ARD covariance functions are learned for each individual feature; therefore, can be used for evaluation of their importance. In that way only a small subset of highly relevant acoustic features is selected, leading to models with better performance and lower complexity. The performance of the proposed method was assessed on two datasets: Parkinson’s disease detection dataset, which contains a range of biomedical voice measurements obtained from 31 subjects, 23 of them suffering from Parkinson’s disease and 8 healthy subjects; and Parkinson’s telemonitoring dataset, containing biomedical voice measurements collected from 42 Parkinson’s disease patients for estimation of the disease progress. Gaussian process classification with automatic relevance determination is able to successfully discriminate between Parkinson’s disease patients and healthy controls with 96.92% accuracy, outperforming Support Vector Machines and decision tree ensembles (random forests, boosted and bagged decision trees). The usability of Gaussian processes is further confirmed in regression task for tracking the progress of the disease.

中文翻译：

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使用高斯过程和自动相关性确定的基于语音的帕金森病估计

摘要 帕金森病是一种进行性神经退行性疾病，常伴有发音、发音、韵律和语言流畅性的障碍。事实上，语言障碍是帕金森病最早的症状之一，可用于早期诊断。我们提出了一项使用高斯过程从语音识别帕金森病和评估疾病进展的实验研究，进一步结合自动相关性确定 (ARD) 以进行有效的特征选择。为每个单独的特征学习 ARD 协方差函数的超参数；因此，可用于评估其重要性。通过这种方式，只选择了一小部分高度相关的声学特征，从而产生具有更好性能和更低复杂性的模型。在两个数据集上评估了所提出方法的性能：帕金森病检测数据集，其中包含从 31 名受试者中获得的一系列生物医学语音测量值，其中 23 名患有帕金森病和 8 名健康受试者；和帕金森远程监测数据集，包含从 42 名帕金森病患者收集的生物医学语音测量值，用于估计疾病进展。具有自动相关性确定的高斯过程分类能够以 96.92% 的准确率成功区分帕金森病患者和健康对照，优于支持向量机和决策树集成（随机森林、提升和袋装决策树）。高斯过程的可用性在用于跟踪疾病进展的回归任务中得到进一步证实。