For several decades now, there has been sporadic interest in automatically characterizing the speech impairment due to Parkinson's disease (PD). Most early studies were confined to quantifying a few speech features that were easy to compute. More recent studies have adopted a machine learning approach where a large number of potential features are extracted and the models are learned automatically from the data. In the same vein, here we characterize the disease using a relatively large cohort of 168 subjects, collected from multiple (three) clinics. We elicited speech using three tasks - the sustained phonation task, the diadochokinetic task and a reading task, all within a time budget of 4 minutes, prompted by a portable device. From these recordings, we extracted 1582 features for each subject using openSMILE, a standard feature extraction tool. We compared the effectiveness of three strategies for learning a regularized regression and find that ridge regression performs better than lasso and support vector regression for our task. We refine the feature extraction to capture pitch-related cues, including jitter and shimmer, more accurately using a time-varying harmonic model of speech. Our results show that the severity of the disease can be inferred from speech with a mean absolute error of about 5.5, explaining 61% of the variance and consistently well-above chance across all clinics. Of the three speech elicitation tasks, we find that the reading task is significantly better at capturing cues than diadochokinetic or sustained phonation task. In all, we have demonstrated that the data collection and inference can be fully automated, and the results show that speech-based assessment has promising practical application in PD. The techniques reported here are more widely applicable to other paralinguistic tasks in clinical domain.

中文翻译：

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从语音全自动评估帕金森病的严重程度。


几十年来，人们对自动表征帕金森病 (PD) 引起的言语障碍产生了零星兴趣。大多数早期研究仅限于量化一些易于计算的语音特征。最近的研究采用了机器学习方法，提取大量潜在特征并从数据中自动学习模型。同样，我们在这里使用从多个（三个）诊所收集的相对较大的 168 名受试者队列来描述该疾病。我们使用三项任务来引发语音——持续发声任务、双动运动任务和阅读任务，所有任务都在便携式设备的提示下，在 4 分钟的时间预算内完成。我们使用标准特征提取工具 openSMILE 从这些录音中提取了每个受试者的 1582 个特征。我们比较了学习正则化回归的三种策略的有效性，发现岭回归对于我们的任务表现优于套索和支持向量回归。我们改进了特征提取，以使用时变语音谐波模型更准确地捕获与音调相关的线索，包括抖动和闪烁。我们的结果表明，可以从言语中推断出疾病的严重程度，平均绝对误差约为 5.5，解释了 61% 的方差，并且在所有诊所中始终远高于概率。在这三个语音启发任务中，我们发现阅读任务在捕捉线索方面明显优于双动运动或持续发声任务。总而言之，我们已经证明数据收集和推理可以完全自动化，结果表明基于语音的评估在 PD 中具有广阔的实际应用前景。 这里报道的技术更广泛地适用于临床领域的其他副语言任务。