Speech, Language, and Hearing Sciences, University of Texas, Austin, Texas, USA,
Mathematics and Statistics, Utah State University, Logan, Utah, USA.
Folia Phoniatr Logop. 2023;75(1):23-34. doi: 10.1159/000525514. Epub 2022 Jun 27.
The goal of this study was to examine the efficacy of acceleration-based articulatory measures in characterizing the decline in speech motor control due to amyotrophic lateral sclerosis (ALS).
Electromagnetic articulography was used to record tongue and lip movements during the production of 20 phrases. Data were collected from 50 individuals diagnosed with ALS. Articulatory kinematic variability was measured using the spatiotemporal index of both instantaneous acceleration and speed signals. Linear regression models were used to analyze the relationship between variability measures and intelligible speaking rate (a clinical measure of disease progression). A machine learning algorithm (support vector regression, SVR) was used to assess whether acceleration or speed features (e.g., mean, median, maximum) showed better performance at predicting speech severity in patients with ALS.
As intelligible speaking rate declined, the variability of acceleration of tongue and lip movement patterns significantly increased (p < 0.001). The variability of speed and vertical displacement did not significantly predict speech performance measures. Additionally, based on R2 and root mean square error (RMSE) values, the SVR model was able to predict speech severity more accurately from acceleration features (R2 = 0.601, RMSE = 38.453) and displacement features (R2 = 0.218, RMSE = 52.700) than from speed features (R2 = 0.554, RMSE = 40.772).
Results from these models highlight differences in speech motor control in participants with ALS. The variability in acceleration of tongue and lip movements increases as speech performance declines, potentially reflecting physiological deviations due to the progression of ALS. Our findings suggest that acceleration is a more sensitive indicator of speech deterioration due to ALS than displacement and speed and may contribute to improved algorithm designs for monitoring disease progression from speech signals.
本研究旨在探讨基于加速度的发音测量在表征肌萎缩侧索硬化症（ALS）导致的言语运动控制下降中的功效。
采用电磁发音仪记录 20 个短语发音过程中的舌部和唇部运动。采集了 50 名确诊 ALS 患者的数据。通过对加速度和速度信号的时空指数进行测量，评估发音运动的时空变异性。使用线性回归模型分析变异性测量与可理解言语率（疾病进展的临床测量指标）之间的关系。使用机器学习算法（支持向量回归，SVR）评估加速度或速度特征（例如均值、中位数、最大值）在预测 ALS 患者言语严重程度方面的表现是否更好。
随着可理解言语率的下降，舌部和唇部运动模式的加速度变异性显著增加（p<0.001）。速度和垂直位移的变异性不能显著预测言语表现指标。此外，基于 R2 和均方根误差（RMSE）值，SVR 模型能够更准确地从加速度特征（R2=0.601，RMSE=38.453）和位移特征（R2=0.218，RMSE=52.700）预测言语严重程度，而不是从速度特征（R2=0.554，RMSE=40.772）预测。
这些模型的结果突显了 ALS 患者言语运动控制的差异。随着言语表现的下降，舌部和唇部运动的加速度变异性增加，这可能反映了 ALS 进展导致的生理偏差。我们的发现表明，与位移和速度相比，加速度是 ALS 导致言语恶化的更敏感指标，可能有助于改进基于言语信号监测疾病进展的算法设计。
