Purpose The purpose of this research note is to provide a performance comparison of available algorithms for the automated evaluation of oral diadochokinesis using speech samples from patients with amyotrophic lateral sclerosis (ALS). Method Four different algorithms based on a wide range of signal processing approaches were tested on a sequential motion rate /pa/-/ta/-/ka/ syllable repetition paradigm collected from 18 patients with ALS and 18 age- and gender-matched healthy controls (HCs). Results The best temporal detection of syllable position for a 10-ms tolerance value was achieved for ALS patients using a traditional signal processing approach based on a combination of filtering in the spectrogram, Bayesian detection, and polynomial thresholding with an accuracy rate of 74.4%, and for HCs using a deep learning approach with an accuracy rate of 87.6%. Compared to HCs, a slow diadochokinetic rate ( p < .001) and diadochokinetic irregularity ( p < .01) were detected in ALS patients. Conclusions The approaches using deep learning or multiple-step combinations of advanced signal processing methods provided a more robust solution to the estimation of oral DDK variables than did simpler approaches based on the rough segmentation of the signal envelope. The automated acoustic assessment of oral diadochokinesis shows excellent potential for monitoring bulbar disease progression in individuals with ALS.

中文翻译：

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肌萎缩侧索硬化症口腔介电运动评估自动声学方法的比较

目的本研究报告的目的是使用肌萎缩性脊髓侧索硬化症 (ALS) 患者的语音样本，对用于自动评估口腔介电运动的可用算法进行性能比较。 方法基于广泛的信号处理方法的四种不同算法在从 18 名 ALS 患者和 18 名年龄和性别匹配的健康对照中收集的顺序运动速率 /pa/-/ta/-/ka/ 音节重复范式上进行了测试（ HC）。 结果使用基于频谱图中的滤波、贝叶斯检测和多项式阈值组合的传统信号处理方法，为 ALS 患者实现了 10 毫秒容差值的最佳音节位置时间检测，准确率为 74.4%，使用深度学习方法对 HC 进行检测，准确率达到 87.6%。与 HC 相比，介电动力学速率较慢 (p< .001) 和介电动力学不规则性 (p< .01) 在 ALS 患者中检测到。 结论与基于信号包络粗分割的简单方法相比，使用深度学习或多步组合的高级信号处理方法为口腔 DDK 变量的估计提供了更稳健的解决方案。口腔介电运动的自动声学评估显示出监测 ALS 患者延髓疾病进展的巨大潜力。